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    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2407.14781"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2111.12267"/>
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	<rdf:li rdf:resource="http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12189/abstract"/>
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  </channel><item rdf:about="https://link.springer.com/article/10.1007/s10955-025-03547-1">
    <title>Error Bounds in a Smooth Metric for Brownian Approximation of Dynamical Systems via Stein’s Method | Journal of Statistical Physics</title>
    <dc:date>2025-12-26T14:24:23+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10955-025-03547-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We adapt Stein’s method of diffusion approximations, developed by Barbour, to the study of chaotic dynamical systems. We establish an error bound in the functional central limit theorem with respect to an integral probability metric of smooth test functions under a functional correlation decay bound. For systems with a sufficiently fast polynomial rate of correlation decay, the error bound is of order 
$O(N^{−1/2})$, under an additional condition on the linear growth of variance. Applications include a family of interval maps with neutral fixed points and unbounded derivatives, and two-dimensional dispersing Sinai billiards."]]></description>
<dc:subject>to:NB central_limit_theorem stochastic_processes dynamical_systems ergodic_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4134e9e7c64c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2407.14781">
    <title>[2407.14781] Bernstein-von Mises theorems for time evolution equations</title>
    <dc:date>2025-07-28T14:19:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2407.14781</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a class of infinite-dimensional dynamical systems driven by non-linear parabolic partial differential equations with initial condition θ modelled by a Gaussian process `prior' probability measure. Given discrete samples of the state of the system evolving in space-time, one obtains updated `posterior' measures on a function space containing all possible trajectories. We give a general set of conditions under which these non-Gaussian posterior distributions are approximated, in Wasserstein distance for the supremum-norm metric, by the law of a Gaussian random function. We demonstrate the applicability of our results to periodic non-linear reaction diffusion equations
\[
\frac{\partial}{\partial t} u - \nabla u = f(u)
\[
\[
u(0) = \theta
\]
where f is any smooth and compactly supported reaction function. In this case the limiting Gaussian measure can be characterised as the solution of a time-dependent Schrödinger equation with `rough' Gaussian initial conditions whose covariance operator we describe."]]></description>
<dc:subject>to:NB stochastic_processes dynamical_systems central_limit_theorem nickl.richard gaussian_processes bayesian_consistency</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4df4e0251977/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nickl.richard"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gaussian_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesian_consistency"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2305.06353">
    <title>[2305.06353] An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference</title>
    <dc:date>2023-06-08T15:30:22+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.06353</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper provides a selective review of the statistical network analysis literature focused on clustering and inference problems for stochastic blockmodels and their variants. We survey asymptotic normality results for stochastic blockmodels as a means of thematically linking classical statistical concepts to contemporary research in network data analysis. Of note, multiple different forms of asymptotically Gaussian behavior arise in stochastic blockmodels and are useful for different purposes, pertaining to estimation and testing, the characterization of cluster structure in community detection, and understanding latent space geometry. This paper concludes with a discussion of open problems and ongoing research activities addressing asymptotic normality and its implications for statistical network modeling."]]></description>
<dc:subject>to:NB stochastic_block_models network_data_analysis central_limit_theorem to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5e2b399628cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_block_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2303.08992">
    <title>[2303.08992] Law of large numbers and central limit theorem for ergodic quantum processes</title>
    <dc:date>2023-04-22T13:55:53+00:00</dc:date>
    <link>https://arxiv.org/abs/2303.08992</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A discrete quantum process is represented by a sequence of quantum operations, which are completely positive maps that are not necessarily trace preserving. We consider quantum processes that are obtained by repeated iterations of a quantum operation with noise. Such ergodic quantum processes generalize independent quantum processes. An ergodic theorem describing convergence to equilibrium for a general class of such processes was recently obtained by Movassagh and Schenker. Under irreducibility and mixing conditions, we obtain a central limit type theorem describing fluctuations around the ergodic limit."

--- Last tag means "mention in further reading, if this checks out".]]></description>
<dc:subject>stochastic_processes quantum_mechanics ergodic_theory mixing central_limit_theorem re:almost_none in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ab8f9922f41/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2301.06632">
    <title>[2301.06632] Asymptotic normality and optimality in nonsmooth stochastic approximation</title>
    <dc:date>2023-01-23T05:38:27+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.06632</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["n their seminal work, Polyak and Juditsky showed that stochastic approximation algorithms for solving smooth equations enjoy a central limit theorem. Moreover, it has since been argued that the asymptotic covariance of the method is best possible among any estimation procedure in a local minimax sense of Hájek and Le Cam. A long-standing open question in this line of work is whether similar guarantees hold for important non-smooth problems, such as stochastic nonlinear programming or stochastic variational inequalities. In this work, we show that this is indeed the case."]]></description>
<dc:subject>optimization stochastic_approximation central_limit_theorem via:mraginsky in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:11866ca1a47b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2111.12603">
    <title>[2111.12603] Strong Invariance Principles for Ergodic Markov Processes</title>
    <dc:date>2022-06-19T17:05:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2111.12603</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Strong invariance principles describe the error term of a Brownian approximation of the partial sums of a stochastic process. While these strong approximation results have many applications, the results for continuous-time settings have been limited. In this paper, we obtain strong invariance principles for a broad class of ergodic Markov processes. Strong invariance principles provide a unified framework for analysing commonly used estimators of the asymptotic variance in settings with a dependence structure. We demonstrate how this can be used to analyse the batch means method for simulation output of Piecewise Deterministic Monte Carlo samplers. We also derive a fluctuation result for additive functionals of ergodic diffusions using our strong approximation results."]]></description>
<dc:subject>central_limit_theorem stochastic_processes convergence_of_stochastic_processes markov_models re:almost_none in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:23ae04823e1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.02148">
    <title>[2206.02148] The Lindeberg-Feller and Lyapunov Conditions in Infinite Dimensions: Asymptotic Normality and Compactness</title>
    <dc:date>2022-06-09T08:33:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.02148</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper generalizes the Lindeberg-Feller and Lyapunov Central Limit Theorems to Hilbert Spaces. Along the way, it proves that the Lindeberg-Feller and Lyapunov conditions force collections of random variables into a nice bounded and compact topological structure. These results will help researchers do non-parametric inference by giving them a simple set of conditions for checking both asymptotic normality as well as compactness and boundedness in infinite-dimensional settings."]]></description>
<dc:subject>to:NB central_limit_theorem hilbert_space probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:62442d917b4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hilbert_space"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.03769">
    <title>[2206.03769] Renormalization group and generalized Central Limit Theorems: The critical probability distributions of the order parameter of the Ising model</title>
    <dc:date>2022-06-09T08:20:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.03769</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show that the functional renormalization group (FRG) allows for the generalization of the central limit theorem to strongly correlated random variables. On the example of the three-dimensional Ising model at criticality and using the simplest implementation of the FRG, we compute the probability distribution functions of the order parameter or equivalently its logarithm, called the rate functions in large deviations theory. We compute the entire family of universal scaling functions, obtained in the limit where the system size L and the correlation length of the infinite system ξ∞ diverge, with the ratio ζ=L/ξ∞ held fixed. It compares very accurately with numerical simulations."]]></description>
<dc:subject>to:NB stochastic_processes random_fields central_limit_theorem ising_model renormalization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ddadeac6846/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ising_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:renormalization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2111.12267">
    <title>[2111.12267] The Practical Scope of the Central Limit Theorem</title>
    <dc:date>2021-12-05T17:06:59+00:00</dc:date>
    <link>https://arxiv.org/abs/2111.12267</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The \textit{Central Limit Theorem (CLT)} is at the heart of a great deal of applied problem-solving in statistics and data science, but the theorem is silent on an important implementation issue: \textit{how much data do you need for the CLT to give accurate answers to practical questions?} Here we examine several approaches to addressing this issue -- along the way reviewing the history of this problem over the last 290 years -- and we illustrate the calculations with case-studies from finite-population sampling and gambling. A variety of surprises emerge."]]></description>
<dc:subject>to:NB central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:438b6a9b9a14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.08469">
    <title>[2107.08469] Quantitative Marcinkiewicz's theorem and central limit theorems: applications to spin systems and point processes</title>
    <dc:date>2021-07-27T12:05:52+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.08469</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The classical Marcinkiewicz theorem states that if an entire characteristic function Ψ of a non-degenerate real-valued random variable X is of the form exp(P(u)) for some polynomial P, then X has to be a Gaussian. In this work, we obtain a broad, quantitative extension of this framework in several directions, establish central limit theorems (CLTs) with explicit rates of convergence, and demonstrate Gaussian fluctuations in continuous spin systems and general classes of point processes. In particular, we obtain quantitative decay estimates on the Kolmogorov-Smirnov distance between X and a Gaussian under the condition that Ψ does not vanish only on a bounded disk. This leads to quantitative CLTs applicable to very general and possibly strongly dependent random systems. In spite of the general applicability, our rates for the CLT match the classic Berry-Esseen bounds for independent sums up to a log factor. We implement this programme for two important classes of models in probability and statistical physics. First, we extend to the setting of continuous spins a popular paradigm for obtaining CLTs for discrete spin systems that is based on the theory of Lee-Yang zeros, focussing in particular on the XY model, Heisenberg ferromagnets and generalised Ising models. Secondly, we establish Gaussian fluctuations for linear statistics of so-called α-determinantal processes for α∈ℝ (including the usual determinantal, Poisson and permanental processes) under very general conditions, including in particular higher dimensional settings where structural alternatives such as random matrix techniques are not available. Our applications demonstrate the significance of having to control the characteristic function only on a (small) disk, and lead to CLTs which, to the best of our knowledge, are not known in generality."]]></description>
<dc:subject>to:NB probability stochastic_processes central_limit_theorem point_processes statistical_mechanics re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9bb1f648cb93/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.00871">
    <title>[2106.00871] A Short and Elementary Proof of the Central Limit Theorem by Individual Swapping</title>
    <dc:date>2021-06-07T01:58:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.00871</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a short proof of the central limit theorem which is elementary in the sense that no knowledge of characteristic functions, linear operators, or other advanced results are needed. Our proof is based on Lindeberg's trick of swapping a term for a normal random variable in turn. The modifications needed to prove the stronger Lindeberg-Feller central limit theorem are addressed at the end."]]></description>
<dc:subject>to:NB probability central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:35f9afccf43f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.12929">
    <title>[2104.12929] Central Limit Theorems for High Dimensional Dependent Data</title>
    <dc:date>2021-04-29T03:27:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.12929</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Motivated by statistical inference problems in high-dimensional time series analysis, we derive non-asymptotic error bounds for Gaussian approximations of sums of high-dimensional dependent random vectors on hyper-rectangles, simple convex sets and sparsely convex sets. We investigate the quantitative effect of temporal dependence on the rates of convergence to normality over three different dependency frameworks (α-mixing, m-dependent, and physical dependence measure). In particular, we establish new error bounds under the α-mixing framework and derive faster rate over existing results under the physical dependence measure. To implement the proposed results in practical statistical inference problems, we also derive a data-driven parametric bootstrap procedure based on a kernel-type estimator for the long-run covariance matrices."]]></description>
<dc:subject>to:NB central_limit_theorem mixing stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1888293e2873/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2009.11861">
    <title>[2009.11861] Functional central limit theorems for epidemic models with varying infectivity</title>
    <dc:date>2021-04-26T14:48:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2009.11861</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we prove functional central limit theorems (FCLTs) for a stochastic epidemic model with varying infectivity and general infectious periods recently introduced in Forien, Pang and Pardoux (2020). The infectivity process (total force of infection at each time) is composed of the independent infectivity random functions of each infectious individual at the elapsed time (that is, infection-age dependent). These infectivity random functions induce the infectious periods (as well as exposed, recovered or immune periods in full generality), whose probability distributions can be very general. The epidemic model includes the generalized non--Markovian SIR, SEIR, SIS, SIRS models with infection-age dependent infectivity. In the FCLT for the generalized SEIR model (including SIR as a special case), the limits for the infectivity and susceptible processes are a unique solution to a two-dimensional Gaussian-driven stochastic Volterra integral equations, and then given these solutions, the limits for the exposed/latent, infected and recovered processes are Gaussian processes expressed in terms of the solutions to those stochastic Volterra integral equations. We also present the FCLTs for the generalized SIS and SIRS models."]]></description>
<dc:subject>to:NB epidemic_models central_limit_theorem stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:87f23cd32476/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.08302">
    <title>[2104.08302] Stein's method of normal approximation: Some recollections and reflections</title>
    <dc:date>2021-04-21T15:01:02+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.08302</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper is a short exposition of Stein's method of normal approximation from my personal perspective. It focuses mainly on the characterization of the normal distribution and the construction of Stein identities. Through examples, it provides glimpses into the many approaches to constructing Stein identities and the diverse applications of Stein's method to mathematical problems. It also includes anecdotes of historical interest, including how Stein discovered his method and how I found an unpublished proof of his of the Berry-Esseen theorem."]]></description>
<dc:subject>to:NB central_limit_theorem probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:59c92292e857/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.10874">
    <title>[2007.10874] Central limit theorems for stationary random fields under weak dependence with application to ambit and mixed moving average fields</title>
    <dc:date>2021-04-08T14:25:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.10874</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We obtain central limit theorems for stationary random fields employing a novel measure of dependence called θ-lex weak dependence. We show that this dependence notion is more general than strong mixing, i.e., it applies to a broader class of models. Moreover, we discuss hereditary properties for θ-lex and η-weak dependence and illustrate the possible applications of the weak dependence notions to the study of the asymptotic properties of stationary random fields. Our general results apply to mixed moving average fields (MMAF in short) and ambit fields. We show general conditions such that MMAF and ambit fields, with the volatility field being an MMAF or a p-dependent random field, are weakly dependent. For all the models mentioned above, we give a complete characterization of their weak dependence coefficients and sufficient conditions to obtain the asymptotic normality of their sample moments. Finally, we give explicit computations of the weak dependence coefficients of MSTOU processes and analyze under which conditions the developed asymptotic theory applies to CARMA fields."]]></description>
<dc:subject>to:NB mixing dependence_measures random_fields central_limit_theorem stochastic_processes to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f6d72975cae5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dependence_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2103.00553">
    <title>[2103.00553] Population Interference in Panel Experiments</title>
    <dc:date>2021-03-26T18:58:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.00553</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in this setting are now readily recognized, and partial remedies are well known. Much less understood is the impact of population interference in panel experiments where treatment is sequentially randomized in the population, and the outcomes are observed at each time step. This paper proposes a general framework for studying population interference in panel experiments and presents new finite population estimation and inference results. Our findings suggest that, under mild assumptions, the addition of a temporal dimension to an experiment alleviates some of the challenges of population interference for certain estimands. In contrast, we show that the presence of carryover effects -- that is, when past treatments may affect future outcomes -- exacerbates the problem. Revisiting the special case of standard experiments with population interference, we prove a central limit theorem under weaker conditions than previous results in the literature and highlight the trade-off between flexibility in the design and the interference structure."]]></description>
<dc:subject>to:NB network_data_analysis causal_inference central_limit_theorem to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b642934adf5d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.11060">
    <title>[1904.11060] Normal Approximation in Large Network Models</title>
    <dc:date>2021-03-03T04:28:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.11060</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a methodology for proving central limit theorems in network models with strategic interactions and homophilous agents. Since data often consists of observations on a single large network, we consider an asymptotic framework in which the network size tends to infinity. In the presence of strategic interactions, network moments are generally complex functions of components, where a node's component consists of all alters to which it is directly or indirectly connected. We find that a modification of "exponential stabilization" conditions from the stochastic geometry literature provides a useful formulation of weak dependence for moments of this type. We establish a CLT for a network moments satisfying stabilization and provide a methodology for deriving primitive sufficient conditions for stabilization using results in branching process theory. We apply the methodology to static and dynamic models of network formation."]]></description>
<dc:subject>to:NB network_data_analysis statistics random_fields central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4680019e3ec7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.14530">
    <title>[2012.14530] On the T-test</title>
    <dc:date>2021-01-03T20:07:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.14530</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The T-test is probably the most popular statistical test; it is routinely recommended by the textbooks. The applicability of the test relies upon the validity of normal or Student's approximation to the distribution of Student's statistic tn. However, the latter assumption is not valid as often as assumed. We show that normal or Student's approximation to Ł(tn) does not hold uniformly even in the class n of samples from zero-mean unit-variance bounded distributions. We present lower bounds to the corresponding error. The fact that a non-parametric test is not applicable uniformly to samples from the class n seems to be established for the first time. It means the T-test can be misleading, and should not be recommended in its present form. We suggest a generalisation of the test that allows for variability of possible limiting/approximating distributions to Ł(tn)."

--- This is not a well-written article (in particular there's a lot of repetition), but the basic point about convergence to the limiting Gaussian or Student distribution being non-uniform and potentially very slow is sound.  The non-appearance of the word "bootstrap" in the paper makes me think the author is almost certainly a probabilist rather than a practicing statistician.]]></description>
<dc:subject>to:NB probability hypothesis_testing to_teach:linear_models central_limit_theorem have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e8e6341bc967/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.15678">
    <title>[2012.15678] On Gaussian Approximation for M-Estimator</title>
    <dc:date>2021-01-03T20:05:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.15678</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This study develops a non-asymptotic Gaussian approximation theory for distributions of M-estimators, which are defined as maximizers of empirical criterion functions. In existing mathematical statistics literature, numerous studies have focused on approximating the distributions of the M-estimators for statistical inference. In contrast to the existing approaches, which mainly focus on limiting behaviors, this study employs a non-asymptotic approach, establishes abstract Gaussian approximation results for maximizers of empirical criteria, and proposes a Gaussian multiplier bootstrap approximation method. Our developments can be considered as an extension of the seminal works (Chernozhukov, Chetverikov and Kato (2013, 2014, 2015)) on the approximation theory for distributions of suprema of empirical processes toward their maximizers. Through this work, we shed new lights on the statistical theory of M-estimators. Our theory covers not only regular estimators, such as the least absolute deviations, but also some non-regular cases where it is difficult to derive or to approximate numerically the limiting distributions such as non-Donsker classes and cube root estimators."]]></description>
<dc:subject>to:NB central_limit_theorem estimation statistics empirical_processes re:HEAS</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:580f8c5bf237/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:HEAS"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.09513">
    <title>[2012.09513] Nearly optimal central limit theorem and bootstrap approximations in high dimensions</title>
    <dc:date>2020-12-18T10:33:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.09513</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of n independent high-dimensional centered random vectors X1,…,Xn over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate. In the case of bounded Xi's, the implied bound for the Kolmogorov distance between the distribution of the scaled average and the Gaussian vector takes the form
C(B2nlog3d/n)1/2logn,
where d is the dimension of the vectors and Bn is a uniform envelope constant on components of Xi's. This bound is sharp in terms of d and Bn, and is nearly (up to logn) sharp in terms of the sample size n. In addition, we show that similar bounds hold for the multiplier and empirical bootstrap approximations. Moreover, we establish bounds that allow for unbounded Xi's, formulated solely in terms of moments of Xi's. Finally, we demonstrate that the bounds can be further improved in some special smooth and zero-skewness cases."]]></description>
<dc:subject>to:NB high-dimensional_probability central_limit_theorem bootstrap</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98fe00635045/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.03486">
    <title>[2012.03486] Asymptotic Normality for Multivariate Random Forest Estimators</title>
    <dc:date>2020-12-10T03:58:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.03486</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Regression trees and random forests are popular and effective non-parametric estimators in practical applications. A recent paper by Athey and Wager shows that the random forest estimate at any point is asymptotically Gaussian; in this paper, we extend this result to the multivariate case and show that the vector of estimates at multiple points is jointly normal. Specifically, the covariance matrix of the limiting normal distribution is diagonal, so that the estimates at any two points are independent in sufficiently deep trees. Moreover, the off-diagonal term is bounded by quantities capturing how likely two points belong to the same partition of the resulting tree. Our results relies on certain a certain stability property when constructing splits, and we give examples of splitting rules for which this assumption is and is not satisfied. We test our proposed covariance bound and the associated coverage rates of confidence intervals in numerical simulations."]]></description>
<dc:subject>to:NB random_forests central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3caac5c75fab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_forests"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2008.05410">
    <title>[2008.05410] Darwinian evolution as Brownian motion on the simplex: A geometric perspective on stochastic replicator dynamics</title>
    <dc:date>2020-08-17T23:45:25+00:00</dc:date>
    <link>https://arxiv.org/abs/2008.05410</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We prove that stochastic replicator dynamics can be interpreted as intrinsic Brownian motion on the simplex equipped the Aitchison geometry. As an immediate consequence we derive three approximation results in the spirit of Wong-Zakai approximation, Donsker's invariance principle and a JKO-scheme. Finally, using the Fokker-Planck equation and Wasserstein-contraction estimates, we study the long time behavior of the stochastic replicator equation, as an example of a non-gradient drift diffusion on the Aitchison simplex."]]></description>
<dc:subject>to:NB replicator_dynamics stochastic_processes central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ddbf5024a96/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replicator_dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.06712">
    <title>[1910.06712] A new CLT for additive functionals of Markov chains</title>
    <dc:date>2019-10-16T14:26:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.06712</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we study the central limit theorem for additive functionals of stationary Markov chains with general state space by using a new idea involving conditioning with respect to both the past and future of the chain. Practically, we show that any stationary and ergodic Markov chain with the variance of partial sums linear in n, satisfies a central limit theorem with a random centering. We do not assume that the Markov chain is irreducible or aperiodic. However, the random centering is not needed if the Markov chain satisfies stronger forms of ergodicity."]]></description>
<dc:subject>to:NB central_limit_theorem markov_models stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:be77a32387cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.bj/1569398764">
    <title>Merlevède , Peligrad , Utev : Functional CLT for martingale-like nonstationary dependent structures</title>
    <dc:date>2019-09-26T00:58:23+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.bj/1569398764</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we develop non-stationary martingale techniques for dependent data. We shall stress the non-stationary version of the projective Maxwell–Woodroofe condition, which will be essential for obtaining maximal inequalities and functional central limit theorem for the following examples: nonstationary ρρ-mixing sequences, functions of linear processes with non-stationary innovations, locally stationary processes, quenched version of the functional central limit theorem for a stationary sequence, evolutions in random media such as a process sampled by a shifted Markov chain."]]></description>
<dc:subject>to:NB central_limit_theorem mixing non-stationarity stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4365022dd63e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-stationarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.06401">
    <title>[1909.06401] Fluctuations for Spatially Extended Hawkes Processes</title>
    <dc:date>2019-09-18T12:56:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.06401</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In a previous paper, it has been shown that the mean-field limit of spatially extended Hawkes processes is characterized as the unique solution u(t,x) of a neural field equation (NFE). The value u(t,x) represents the membrane potential at time t of a typical neuron located in position x, embedded in an infinite network of neurons. In the present paper, we complement this result by studying the fluctuations of such a stochastic system around its mean field limit u(t,x). Our first main result is a central limit theorem stating that the spatial distribution associated to these fluctuations converges to the unique solution of some stochastic differential equation driven by a Gaussian noise. In our second main result we show that the solutions of this stochastic differential equation can be well approximated by a stochastic version of the neural field equation satisfied by u(t,x). To the best of our knowledge, this result appears to be new in the literature."]]></description>
<dc:subject>to:NB point_processes stochastic_processes random_fields central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fd12d1a0a806/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.08686">
    <title>[1809.08686] On the quenched CLT for stationary random fields under projective criteria</title>
    <dc:date>2019-09-15T14:46:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.08686</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Motivated by random evolutions which do not start from equilibrium, in a recent work, Peligrad and Volný (2018) showed that the quenched CLT (central limit theorem) holds for ortho-martingale random fields. In this paper, we study the quenched CLT for a class of random fields larger than the ortho-martingales. To get the results, we impose sufficient conditions in terms of projective criteria under which the partial sums of a stationary random field admit an ortho-martingale approximation. More precisely, the sufficient conditions are of the Hannan's projective type. As applications, we establish quenched CLT's for linear and nonlinear random fields with independent innovations."]]></description>
<dc:subject>to:NB stochastic_processes random_fields martingales central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:138affc41fd1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.03238">
    <title>[1909.03238] Linear response and moderate deviations: hierarchical approach. V</title>
    <dc:date>2019-09-15T14:29:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.03238</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Moderate Deviations Principle (MDP) is well-understood for sums of independent random variables, worse understood for stationary random sequences, and scantily understood for random fields. Here it is established for some planary random fields of the form Xt=ψ(Gt) obtained from a Gaussian random field Gt via a function ψ, and consequently, for zeroes of the Gaussian Entire Function."]]></description>
<dc:subject>to:NB central_limit_theorem random_fields stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b61503a99002/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1804.09220">
    <title>[1804.09220] A Useful Version of the Central Limit Theorem for a General Class of Markov Chains</title>
    <dc:date>2019-09-13T13:21:46+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.09220</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the paper we propose certain conditions, relatively easy to verify, which ensure the central limit theorem for some general class of Markov chains. To justify the usefulness of our criterion, we further verify it for a particular discrete-time Markov dynamical system. From the application point of view, the examined system provides a useful tool in analysing the stochastic dynamics of gene expression in prokaryotes."]]></description>
<dc:subject>to:NB central_limit_theorem markov_models stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d5921be46a67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.01757">
    <title>[1907.01757] Cramér type moderate deviations for stationary sequences of bounded random variables</title>
    <dc:date>2019-07-17T20:46:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.01757</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We derive Cramér type moderate deviations for stationary sequences of bounded random variables. Our results imply the moderate deviation principles and a Berry-Esseen bound. Applications to quantile coupling inequalities, functions of ϕ-mixing sequences, and contracting Markov chains are discussed."]]></description>
<dc:subject>to:NB deviation_inequalities central_limit_theorem large_deviations stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9ce248a68057/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deviation_inequalities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.11913">
    <title>[1905.11913] Maximal correlation and the rate of Fisher information convergence in the Central Limit Theorem</title>
    <dc:date>2019-05-29T21:18:38+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.11913</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the behaviour of the Fisher information of scaled sums of independent and identically distributed random variables in the Central Limit Theorem regime. We show how this behaviour can be related to the second-largest non-trivial eigenvalue associated with the Hirschfeld--Gebelein--Rényi maximal correlation. We prove that assuming this eigenvalue satisfies a strict inequality, an O(1/n) rate of convergence and a strengthened form of monotonicity hold."]]></description>
<dc:subject>to:NB central_limit_theorem dependence_measures probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3292460efd7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dependence_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.bj/1551862848">
    <title>Lee , Song : Stable limit theorems for empirical processes under conditional neighborhood dependence</title>
    <dc:date>2019-05-25T03:01:30+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.bj/1551862848</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper introduces a new concept of stochastic dependence among many random variables which we call conditional neighborhood dependence (CND). Suppose that there are a set of random variables and a set of sigma algebras where both sets are indexed by the same set endowed with a neighborhood system. When the set of random variables satisfies CND, any two non-adjacent sets of random variables are conditionally independent given sigma algebras having indices in one of the two sets’ neighborhood. Random variables with CND include those with conditional dependency graphs and a class of Markov random fields with a global Markov property. The CND property is useful for modeling cross-sectional dependence governed by a complex, large network. This paper provides two main results. The first result is a stable central limit theorem for a sum of random variables with CND. The second result is a Donsker-type result of stable convergence of empirical processes indexed by a class of functions satisfying a certain bracketing entropy condition when the random variables satisfy CND."]]></description>
<dc:subject>to_read empirical_processes random_fields stochastic_processes central_limit_theorem in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f93b70f7625b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/functional-gaussian-approximation-for-dependent-structures-9780198826941?cc=us&amp;lang=en#">
    <title>Functional Gaussian Approximation for Dependent Structures - Florence Merlevede; Magda Peligrad; Sergey Utev - Oxford University Press</title>
    <dc:date>2019-05-24T23:55:52+00:00</dc:date>
    <link>https://global.oup.com/academic/product/functional-gaussian-approximation-for-dependent-structures-9780198826941?cc=us&amp;lang=en#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Functional Gaussian Approximation for Dependent Structures develops and analyses mathematical models for phenomena that evolve in time and influence each another. It provides a better understanding of the structure and asymptotic behaviour of stochastic processes. 
"Two approaches are taken. Firstly, the authors present tools for dealing with the dependent structures used to obtain normal approximations. Secondly, they apply normal approximations to various examples. The main tools consist of inequalities for dependent sequences of random variables, leading to limit theorems, including the functional central limit theorem and functional moderate deviation principle. The results point out large classes of dependent random variables which satisfy invariance principles, making possible the statistical study of data coming from stochastic processes both with short and long memory.
"The dependence structures considered throughout the book include the traditional mixing structures, martingale-like structures, and weakly negatively dependent structures, which link the notion of mixing to the notions of association and negative dependence. Several applications are carefully selected to exhibit the importance of the theoretical results. They include random walks in random scenery and determinantal processes. In addition, due to their importance in analysing new data in economics, linear processes with dependent innovations will also be considered and analysed."]]></description>
<dc:subject>to:NB central_limit_theorem stochastic_processes convergence_of_stochastic_processes mixing ergodic_theory re:almost_none books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a47e5db7c861/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12189/abstract">
    <title>Optimal Rate of Convergence for Empirical Quantiles and Distribution Functions for Time Series - Jirak - 2016 - Journal of Time Series Analysis - Wiley Online Library</title>
    <dc:date>2016-10-18T20:58:30+00:00</dc:date>
    <link>http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12189/abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Given a stationary sequence  , we are interested in the rate of convergence in the central limit theorem of the empirical quantiles and the empirical distribution function. Under a general notion of weak dependence, we show a Berry–Esseen result with optimal rate n−1/2. The setup includes many prominent time series models, such as functions of ARMA or (augmented) GARCH processes. In this context, optimal Berry–Esseen rates for empirical quantiles appear to be novel."]]></description>
<dc:subject>to:NB empirical_processes nonparametrics statistics time_series central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5f76a7a363f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/BF01902875">
    <title>Rate of convergence in the central limit theorem and in the strong law of large numbers for von mises statistics | SpringerLink</title>
    <dc:date>2016-10-06T15:13:19+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/BF01902875</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper provides the rate of convergence in the central limit theorem and in the strong law of large numbers forvon Mises statistics VN=N−m∑i1=1N…∑im=1Nh(Xi1,…,Xim),N⩾m, based on i.i.d. random variablesX1,..., XN.
"The proofs rely on a decomposition ofvon Mises statistics into a linear combination ofU-statistics and then use (generalized) results on the convergence rates forU-statistics obtained byGrams/Serfling [1973] andCallaert/Janssen [1978]."]]></description>
<dc:subject>to:NB asymptotics central_limit_theorem u-statistics re:network_bootstraps</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:326b6a23229d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:asymptotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:u-statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:network_bootstraps"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.aop/1176992632">
    <title>Barron : Entropy and the Central Limit Theorem</title>
    <dc:date>2016-04-15T21:44:51+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.aop/1176992632</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A strengthened central limit theorem for densities is established showing monotone convergence in the sense of relative entropy."

--- Now _that_ is how you write an abstract.]]></description>
<dc:subject>to:NB to_read information_theory central_limit_theorem probability barron.andrew_w. re:almost_none via:tslumley</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8c0f85a0ad8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barron.andrew_w."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tslumley"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.aop/1176989128">
    <title>Arcones , Gine : Limit Theorems for $U$-Processes</title>
    <dc:date>2015-12-09T00:27:53+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.aop/1176989128</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Necessary and sufficient conditions for the law of large numbers and sufficient conditions for the central limit theorem for U-processes are given. These conditions are in terms of random metric entropies. The CLT and LLN for VC subgraph classes of functions as well as for classes satisfying bracketing conditions follow as consequences of the general results. In particular, Liu's simplicial depth process satisfies both the LLN and the CLT. Among the techniques used, randomization, decoupling inequalities, integrability of Gaussian and Rademacher chaos and exponential inequalities for U-statistics should be mentioned."]]></description>
<dc:subject>u-statistics empirical_processes deviation_inequalities vc-dimension central_limit_theorem re:smoothing_adjacency_matrices in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fbf2713f12b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:u-statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deviation_inequalities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vc-dimension"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:smoothing_adjacency_matrices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/euclid.aop/1176990448">
    <title>Gotze : On the Rate of Convergence in the Multivariate CLT</title>
    <dc:date>2014-11-20T17:38:14+00:00</dc:date>
    <link>http://projecteuclid.org/euclid.aop/1176990448</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Berry-Esseen theorems are proved in the multidimensional central limit theorem without using Fourier methods. An effective and simple estimate of the error in the CLT for sums and convex sets using Stein's method and induction is derived. Furthermore, the error in the CLT for multivariate functions of independent random elements is estimated extending results of van Zwet and Friedrich to the multivariate case."]]></description>
<dc:subject>to:NB probability central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53050fa9e714/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/academic/subjects/mathematics/abstract-analysis/probability-classical-limit-theorems?format=HB">
    <title>Probability: The Classical Limit Theorems</title>
    <dc:date>2014-10-28T23:24:23+00:00</dc:date>
    <link>http://www.cambridge.org/us/academic/subjects/mathematics/abstract-analysis/probability-classical-limit-theorems?format=HB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Probability theory has been extraordinarily successful at describing a variety of phenomena, from the behaviour of gases to the transmission of messages, and is, besides, a powerful tool with applications throughout mathematics. At its heart are a number of concepts familiar in one guise or another to many: Gauss' bell-shaped curve, the law of averages, and so on, concepts that crop up in so many settings they are in some sense universal. This universality is predicted by probability theory to a remarkable degree. This book explains that theory and investigates its ramifications. Assuming a good working knowledge of basic analysis, real and complex, the author maps out a route from basic probability, via random walks, Brownian motion, the law of large numbers and the central limit theorem, to aspects of ergodic theorems, equilibrium and nonequilibrium statistical mechanics, communication over a noisy channel, and random matrices."]]></description>
<dc:subject>probability books:noted ergodic_theory central_limit_theorem stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8899e7cf990e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1404.1392">
    <title>[1404.1392] A short survey of Stein's method</title>
    <dc:date>2014-04-17T18:00:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.1392</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Stein's method is a powerful technique for proving central limit theorems in probability theory when more straightforward approaches cannot be implemented easily. This article begins with a survey of the historical development of Stein's method and some recent advances. This is followed by a description of a "general purpose" variant of Stein's method that may be called the generalized perturbative approach, and an application of this method to minimal spanning trees. The article concludes with the descriptions of some well known open problems that may possibly be solved by the perturbative approach or some other variant of Stein's method."]]></description>
<dc:subject>to:NB probability stochastic_processes central_limit_theorem steins_method to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7ba50392cd81/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:steins_method"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1404.0645">
    <title>[1404.0645] Moment bounds and concentration inequalities for slowly mixing dynamical systems</title>
    <dc:date>2014-04-14T19:42:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.0645</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We obtain optimal moment bounds for Birkhoff sums, and optimal concentration inequalities, for a large class of slowly mixing dynamical systems, including those that admit anomalous diffusion in the form of a stable law or a central limit theorem with nonstandard scaling (nlogn)1/2."]]></description>
<dc:subject>mixing concentration_of_measure dynamical_systems central_limit_theorem stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6437bba409a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/euclid.aop/1393251303">
    <title>Berkes , Liu , Wu : Komlós–Major–Tusnády approximation under dependence</title>
    <dc:date>2014-03-12T19:14:12+00:00</dc:date>
    <link>http://projecteuclid.org/euclid.aop/1393251303</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The celebrated results of Komlós, Major and Tusnády [Z. Wahrsch. Verw. Gebiete 32 (1975) 111–131; Z. Wahrsch. Verw. Gebiete 34 (1976) 33–58] give optimal Wiener approximation for the partial sums of i.i.d. random variables and provide a powerful tool in probability and statistics. In this paper we extend KMT approximation for a large class of dependent stationary processes, solving a long standing open problem in probability theory. Under the framework of stationary causal processes and functional dependence measures of Wu [Proc. Natl. Acad. Sci. USA 102 (2005) 14150–14154], we show that, under natural moment conditions, the partial sum processes can be approximated by Wiener process with an optimal rate. Our dependence conditions are mild and easily verifiable. The results are applied to ergodic sums, as well as to nonlinear time series and Volterra processes, an important class of nonlinear processes."]]></description>
<dc:subject>to:NB mixing ergodic_theory convergence_of_stochastic_processes central_limit_theorem stochastic_processes re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e3a22f3fd2d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.4266">
    <title>[1310.4266] An invariance principle under the total variation distance</title>
    <dc:date>2013-10-23T14:20:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.4266</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Let X1,X2,… be a sequence of i.i.d. random variables, with mean zero and variance one. Let Wn=(X1+…+Xn)/n‾‾√. An old and celebrated result of Prohorov asserts that Wn converges in total variation to the standard Gaussian distribution if and only if Wn0 has an absolutely continuous component for some n0. In the present paper, we give yet another proof and extend Prohorov's theorem to a situation where, instead of Wn, we consider more generally a sequence of homogoneous polynomials in the Xi. More precisely, we exhibit conditions for a recent invariance principle proved by Mossel, O'Donnel and Oleszkiewicz to hold under the total variation distance. There are many works about CLT under various metrics in the literature, but the present one seems to be the first attempt to deal with homogeneous polynomials in the Xi with degree strictly greater than one."]]></description>
<dc:subject>to:NB central_limit_theorem stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:50feca6d190e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.org/proceedings/papers/v31/sinn13a.html">
    <title>Central Limit Theorems for Conditional Markov Chains | AISTATS 2013 | JMLR W&amp;CP</title>
    <dc:date>2013-09-27T13:16:38+00:00</dc:date>
    <link>http://jmlr.org/proceedings/papers/v31/sinn13a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies Central Limit Theorems for real-valued functionals of Conditional Markov Chains. Using a classical result by Dobrushin (1956) for non-stationary Markov chains, a conditional Central Limit Theorem for fixed sequences of observations is established. The asymptotic variance can be estimated by resampling the latent states conditional on the observations. If the conditional means themselves are asymptotically normally distributed, an unconditional Central Limit Theorem can be obtained. The methodology is used to construct a statistical hypothesis test which is applied to synthetically generated environmental data."]]></description>
<dc:subject>have_read central_limit_theorem markov_models stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5a4b4ccbee22/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.6906">
    <title>[1212.6906] Central Limit Theorems and Multiplier Bootstrap when p is much larger than n</title>
    <dc:date>2013-09-09T03:36:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.6906</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We derive a central limit theorem for the maximum of a sum of high dimensional random vectors. Specifically, we establish conditions under which the distribution of the maximum is approximated by that of the maximum of a sum of the Gaussian random vectors with the same covariance matrices as the original vectors. The key innovation of this result is that it applies even when the dimension of random vectors (p) is large compared to the sample size (n); in fact, p can be much larger than n. We also show that the distribution of the maximum of a sum of the random vectors with unknown covariance matrices can be consistently estimated by the distribution of the maximum of a sum of the conditional Gaussian random vectors obtained by multiplying the original vectors with i.i.d. Gaussian multipliers. This is the multiplier bootstrap procedure. Here too, p can be large or even much larger than n. These distributional approximations, either Gaussian or conditional Gaussian, yield a high-quality approximation to the distribution of the original maximum, often with approximation error decreasing polynomially in the sample size, and hence are of interest in many applications. We demonstrate how our central limit theorem and the multiplier bootstrap can be used for high dimensional estimation, multiple hypothesis testing, and adaptive specification testing. All these results contain non-asymptotic bounds on approximation errors. "

- For the reading group this week.]]></description>
<dc:subject>to:NB empirical_processes stochastic_processes approximation central_limit_theorem bootstrap statistics probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:038f7e73d1e5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s10959-011-0377-0">
    <title>Convergence to Stable Laws in Relative Entropy - Springer</title>
    <dc:date>2013-08-19T03:48:23+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s10959-011-0377-0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Convergence to stable laws in relative entropy is established for sums of i.i.d. random variables."

- Now _that_ is an abstract.]]></description>
<dc:subject>to:NB probability central_limit_theorem information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70cf953e33b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.7960">
    <title>[1304.7960] A strictly stationary $beta$-mixing process satisfying the central limit theorem but not the weak invariance principle</title>
    <dc:date>2013-05-01T16:31:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.7960</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In 1983, N. Herrndorf proved that for a $\phi$-mixing sequence satisfying the central limit theorem and $\phi(1)<1$, the weak invariance principle takes place. The question whether for strictly stationary sequences with finite second moments and a weaker type ($\alpha$, $\beta$, $\rho$) of mixing the central limit theorem implies the weak invariance principle remained open. "
We construct a strictly stationary $\beta$-mixing sequence with finite second moments for which the central limit theorem takes place but not the weak invariance principle.]]></description>
<dc:subject>to:NB mixing central_limit_theorem re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4f6bd9420402/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.4580">
    <title>[1304.4580] Quenched Invariance Principles via Martingale Approximation</title>
    <dc:date>2013-04-17T13:05:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.4580</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we survey the almost sure central limit theorem and its functional form (quenched) for stationary and ergodic processes. For additive functionals of a stationary and ergodic Markov chain these theorems are known under the terminology of central limit theorem and its functional form, started at a point. All these results have in common that they are obtained via a martingale approximation in the almost sure sense. We point out several applications of these results to classes of mixing sequences, shift processes, reversible Markov chains, Metropolis Hastings algorithms."]]></description>
<dc:subject>to:NB stochastic_processes central_limit_theorem markov_models ergodic_theory martingales re:almost_none monte_carlo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7401f9e40441/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:monte_carlo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.2621">
    <title>[1304.2621] Central limit theorems in linear dynamics</title>
    <dc:date>2013-04-10T21:29:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.2621</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Given a bounded operator $T$ on a Banach space $X$, we study the existence of a probability measure $\mu$ on $X$ such that, for many functions $f:X\to\mathbb K$, the sequence $(f+\dots+f\circ T^{n-1})/\sqrt n$ converges in distribution to a Gaussian random variable."]]></description>
<dc:subject>to:NB mixing central_limit_theorem ergodic_theory stochastic_processes dynamical_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee98841bea95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/knowledge/isbn/item6676473/?site_locale=en_US">
    <title>Normal Approximations with Malliavin Calculus - Academic and Professional Books - Cambridge University Press</title>
    <dc:date>2013-03-29T20:41:04+00:00</dc:date>
    <link>http://www.cambridge.org/us/knowledge/isbn/item6676473/?site_locale=en_US</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Stein's method is a collection of probabilistic techniques that allow one to assess the distance between two probability distributions by means of differential operators. In 2007, the authors discovered that one can combine Stein's method with the powerful Malliavin calculus of variations, in order to deduce quantitative central limit theorems involving functionals of general Gaussian fields. This book provides an ideal introduction both to Stein's method and Malliavin calculus, from the standpoint of normal approximations on a Gaussian space. Many recent developments and applications are studied in detail, for instance: fourth moment theorems on the Wiener chaos, density estimates, Breuer–Major theorems for fractional processes, recursive cumulant computations, optimal rates and universality results for homogeneous sums. Largely self-contained, the book is perfect for self-study. It will appeal to researchers and graduate students in probability and statistics, especially those who wish to understand the connections between Stein's method and Malliavin calculus."]]></description>
<dc:subject>to:NB books:noted probability central_limit_theorem stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2d46c538b092/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aap/1363354109">
    <title>Limnios , Swishchuk : Discrete-Time Semi-Markov Random Evolutions and their Applications</title>
    <dc:date>2013-03-15T16:12:15+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aap/1363354109</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we introduce discrete-time semi-Markov random evolutions (DTSMREs) and study asymptotic properties, namely, averaging, diffusion approximation, and diffusion approximation with equilibrium by the martingale weak convergence method. The controlled DTSMREs are introduced and Hamilton–Jacobi–Bellman equations are derived for them. The applications here concern the additive functionals (AFs), geometric Markov renewal chains (GMRCs), and dynamical systems (DSs) in discrete time. The rates of convergence in the limit theorems for DTSMREs and AFs, GMRCs, and DSs are also presented."]]></description>
<dc:subject>to:NB stochastic_processes markov_models central_limit_theorem convergence_of_stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d90b8f007568/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aop/1362750942">
    <title>Kuelbs , Kurtz , Zinn : A CLT for empirical processes involving time-dependent data</title>
    <dc:date>2013-03-10T01:14:54+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aop/1362750942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For stochastic processes {Xt : t∈E}, we establish sufficient conditions for the empirical process based on {IXt≤y−Pr(Xt≤y) : t∈E,y∈ℝ} to satisfy the CLT uniformly in t∈E, y∈ℝ. Corollaries of our main result include examples of classical processes where the CLT holds, and we also show that it fails for Brownian motion tied down at zero and E=[0,1]."]]></description>
<dc:subject>to:NB empirical_processes stochastic_processes central_limit_theorem kurtz.thomas_g.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9da8e56eddc3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurtz.thomas_g."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6362212">
    <title>IEEE Xplore - Limit Theorems in Hidden Markov Models</title>
    <dc:date>2013-02-21T18:00:02+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6362212</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle, and a variant of the Chernoff bound in finite-state hidden Markov models. These limit theorems are of interest in certain areas of information theory and statistics. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov models."]]></description>
<dc:subject>to:NB markov_models ergodic_theory central_limit_theorem re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:091a6651155b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.3740">
    <title>[1302.3740] Strong approximations for long memory sequences based partial sums, counting and their Vervaat processes</title>
    <dc:date>2013-02-18T17:38:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.3740</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the asymptotic behaviour of partial sums of long range dependent random variables and that of their counting process, together with an appropriately normalized integral process of the sum of these two processes, the so-called Vervaat process. The first two of these processes are approximated by an appropriately constructed fractional Brownian motion, while the Vervaat process in turn is approximated by the square of the same fractional Brownian motion."]]></description>
<dc:subject>to:NB central_limit_theorem stochastic_processes long-range_dependence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dd322ba8f5a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:long-range_dependence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/chapter/10.1007%2F978-3-642-33305-7_10">
    <title>Central Limit Theorems for Weakly Dependent Random Fields - Springer</title>
    <dc:date>2013-02-12T01:43:44+00:00</dc:date>
    <link>http://link.springer.com/chapter/10.1007%2F978-3-642-33305-7_10</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This chapter is a primer on the limit theorems for dependent random fields. First, dependence concepts such as mixing, association and their generalizations are introduced. Then, moment inequalities for sums of dependent random variables are stated which yield e.g. the asymptotic behaviour of the variance of these sums which is essential for the proof of limit theorems. Finally, central limit theorems for dependent random fields are given. Applications to excursion sets of random fields and Newman’s conjecture in the absence of finite susceptibility are discussed as well."]]></description>
<dc:subject>to:NB central_limit_theorem stochastic_processes mixing random_fields</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d234ea141b38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio?isbn=978-3642150067">
    <title>Normal Approximation by Stein's Method (Probability and Its Applications) by Louis H. Y. Chen - Powell's Books</title>
    <dc:date>2013-01-16T23:58:25+00:00</dc:date>
    <link>http://www.powells.com/biblio?isbn=978-3642150067</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Since its introduction in 1972, Stein's method has offered a completely novel way of evaluating the quality of normal approximations. Through its characterizing equation approach, it is able to provide approximation error bounds in a wide variety of situations, even in the presence of complicated dependence. Use of the method thus opens the door to the analysis of random phenomena arising in areas including statistics, physics, and molecular biology. Though Stein's method for normal approximation is now mature, the literature has so far lacked a complete self contained treatment. This volume contains thorough coverage of the method's fundamentals, includes a large number of recent developments in both theory and applications, and will help accelerate the appreciation, understanding, and use of Stein's method by providing the reader with the tools needed to apply it in new situations. It addresses researchers as well as graduate students in Probability, Statistics and Combinatorics."]]></description>
<dc:subject>books:noted probability central_limit_theorem steins_method re:almost_none via:jin.jiashun in_NB books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a0ec021f442c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:steins_method"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jin.jiashun"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s00440-011-0371-6">
    <title>Central limit theorem for triangular arrays of non-homogeneous Markov chains - Springer</title>
    <dc:date>2012-12-03T00:09:31+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s00440-011-0371-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we obtain the central limit theorem for triangular arrays of non-homogeneous Markov chains under a condition imposed to the maximal coefficient of correlation. The proofs are based on martingale techniques and a sharp lower bound estimate for the variance of partial sums. The results complement an important central limit theorem of Dobrushin based on the contraction coefficient."]]></description>
<dc:subject>to:NB central_limit_theorem mixing markov_models stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:269b080dba60/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1208.3783">
    <title>[1208.3783] Central limit theorems and diffusion approximations for multiscale Markov chain models</title>
    <dc:date>2012-08-24T11:25:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.3783</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Ordinary differential equations obtained as limits of Markov processes appear in many settings. They may arise by scaling large systems, or by averaging rapidly fluctuating systems, or in systems involving multiple time-scales, by a combination of the two. Motivated by models with multiple time-scales arising in systems biology, we present a general approach to proving a central limit theorem capturing the fluctuations of the original model around the deterministic limit. The central limit theorem provides a method for deriving an appropriate diffusion (Langevin) approximation."]]></description>
<dc:subject>stochastic_processes stochastic_differential_equations central_limit_theorem markov_models convergence_of_stochastic_processes kurtz.thomas_g. re:almost_none in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:adcca19056ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurtz.thomas_g."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.6813">
    <title>[1206.6813] A concentration theorem for projections</title>
    <dc:date>2012-07-09T03:46:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6813</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scale-mixture of spherical Gaussians -- specifically, a mixture of distributions N(0, sigma^2 I_d) where the weight of the particular sigma component is P (| X |^2 = sigma^2 D). The extent of this effect depends upon the ratio of d to D, and upon a particular coefficient of eccentricity of X's distribution. We explore this result in a variety of experiments."

(How is this not just the old Diaconis-Freedman result?)]]></description>
<dc:subject>to:NB high-dimensional_probability central_limit_theorem</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:350c9c34e2be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.longstoryshortpier.com/2012/05/09/clews">
    <title>Long story; short pier: Clews</title>
    <dc:date>2012-05-10T13:32:14+00:00</dc:date>
    <link>http://www.longstoryshortpier.com/2012/05/09/clews</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["And the time ... in physics class, when we were doing these basic (very basic) labs on probability, and I had a little handheld pachinko machine? With a bunch of balls, and evenly spaced rods, and stalls at the bottom? And you tilt it down, and all the balls roll to the top, and you tilt it back, and they come cascading down, and hit the rods, and either bounce left or right, and in the end you’ve got this lovely little bell curve of balls at the bottom, because law of averages and such most balls bounce left, then right, then left, or some combination thereof, and end up in the middle? And only a few go left-left-left-left, or right-right-right-right, and end up on either end? —Anyway, it’s my turn, so I tilt it down, then back again, and click-clack-click-clack-click, and wouldn’t you know it, I’ve got an almost perfect reverse bell curve. Towering stacks of balls to the left and right, and almost nothing at all in the middle.
"So I go to the teacher running the show and hold it out to him and say, okay, now what, smart guy? (“If it fails to agree, under novel experiments or with refined measuring techniques, it is not said that one should not be happy.”)
"And the teacher looks at the little handheld pachinko machine, cocks an eyebrow, tilts it down, tilts it back, clack-click-clack-click-clack. Perfect bell curve.
"“There,” he says. “Fixed it for you.”
"—And I can’t for the life of me tell you which of those gestures is the argument with the universe, and which the sermon on the way things ought to be, dammit. —And that might just be my problem."]]></description>
<dc:subject>funny:geeky probability central_limit_theorem to_teach at_that_moment_the_student_was_enlightened</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c71f24f71c46/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:geeky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:at_that_moment_the_student_was_enlightened"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1202.4875">
    <title>[1202.4875] A quenched invariance principle for stationary processes</title>
    <dc:date>2012-03-05T13:04:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1202.4875</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this note, we prove a conditionally centered version of the quenched weak invariance principle under the Hannan condition, for stationary processes. In the course, we obtain a (new) construction of the fact that any stationary process may be seen as a functional of a Markov chain."]]></description>
<dc:subject>stochastic_processes central_limit_theorem markovian_representations ergodic_theory re:almost_none re:AoS_project in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8e41d6a0373/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markovian_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.numdam.org/item?id=AIHPB_1995__31_2_393_0">
    <title>Doukhan, Massart, Rio: Invariance principles for absolutely regular empirical processes</title>
    <dc:date>2012-02-24T05:15:01+00:00</dc:date>
    <link>http://www.numdam.org/item?id=AIHPB_1995__31_2_393_0</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>empirical_processes stochastic_processes mixing central_limit_theorem to_read re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:227ed863c23e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1201.4579">
    <title>[1201.4579] Limit theorems for stationary Markov processes with L2-spectral gap</title>
    <dc:date>2012-01-28T16:51:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.4579</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Let $(X_t, Y_t)_{tin T}$ be a discrete or continuous-time Markov process with state space $X times R^d$ where $X$ is an arbitrary measurable set. Its transition semigroup is assumed to be additive with respect to the second component, i.e. $(X_t, Y_t)_{tin T}$ is assumed to be a Markov additive process. In particular, this implies that the first component $(X_t)_{tin T}$ is also a Markov process. Markov random walks or additive functionals of a Markov process are special instances of Markov additive processes. In this paper, the process $(Y_t)_{tin T}$ is shown to satisfy the following classical limit theorems: (a) the central limit theorem, (b) the local limit theorem, (c) the one-dimensional Berry-Esseen theorem, (d) the one-dimensional first-order Edgeworth expansion, provided that we have sup{tin(0,1]cap T : E{pi,0}[|Y_t| ^{alpha}] < 1 with the expected order with respect to the independent case (up to some $varepsilon > 0$ for (c) and (d)). For the statements (b) and (d), a Markov nonlattice condition is also assumed as in the independent case. All the results are derived under the assumption that the Markov process $(X_t)_{tin T}$ has an invariant probability distribution $pi$, is stationary and has the $L^2(pi)$-spectral gap property (that is, $(X_t)tin N}$ is $rho$-mixing in the discrete-time case). The case where $(X_t)_{tin T}$ is non-stationary is briefly discussed. As an application, we derive a Berry-Esseen bound for the M-estimators associated with $rho$-mixing Markov chains."]]></description>
<dc:subject>markov_models stochastic_processes central_limit_theorem mixing ergodic_theory in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b63611506168/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1201.2256">
    <title>[1201.2256] Empirical Processes of Markov Chains and Dynamical Systems Indexed by Classes of Functions</title>
    <dc:date>2012-01-18T17:55:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.2256</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study weak convergence of empirical processes of dependent data, indexed by classes of functions. We obtain results that are especially suitable for data arising from dynamical systems and Markov chains, where the Central Limit Theorem for partial sums is commonly derived via the spectral gap technique. Our results apply, e.g. to the empirical process of ergodic torus automorphisms."]]></description>
<dc:subject>empirical_processes stochastic_processes markov_models central_limit_theorem dynamical_systems in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:41dcc808df40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aop/1321539125">
    <title>Berkes , Hörmann , Schauer : Split invariance principles for stationary processes</title>
    <dc:date>2011-11-19T16:49:48+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aop/1321539125</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The results of Komlós, Major and Tusnády give optimal Wiener approximation of partial sums of i.i.d. random variables and provide an extremely powerful tool in probability and statistical inference. Recently Wu [Ann. Probab. 35 (2007) 2294–2320] obtained Wiener approximation of a class of dependent stationary processes with finite pth moments, 2 < p ≤ 4, with error term o(n1/p(log n)γ), γ > 0, and Liu and Lin [Stochastic Process. Appl. 119 (2009) 249–280] removed the logarithmic factor, reaching the Komlós–Major–Tusnády bound o(n1/p). No similar results exist for p > 4, and in fact, no existing method for dependent approximation yields an a.s. rate better than o(n1/4). In this paper we show that allowing a second Wiener component in the approximation, we can get rates near to o(n1/p) for arbitrary p > 2. This extends the scope of applications of the results essentially, as we illustrate it by proving new limit theorems for increments of stochastic processes and statistical tests for short term (epidemic) changes in stationary processes. Our method works under a general weak dependence condition covering wide classes of linear and nonlinear time series models and classical dynamical systems."]]></description>
<dc:subject>to:NB stochastic_processes convergence_of_stochastic_processes central_limit_theorem re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:12cdc497efc3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:convergence_of_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1111.4073">
    <title>[1111.4073] Multivariate Normal Approximation by Stein's Method: The Concentration Inequality Approach</title>
    <dc:date>2011-11-19T16:48:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1111.4073</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The concentration inequality approach for normal approximation by Stein's method is generalized to the multivariate setting. This approach is used to prove a multivariate normal approximation theorem for standardized sums of independent random vectors with an error bound of the order $k^{1/2}gamma$, where $k$ is the dimension of the random vectors and $gamma$ is the sum of absolute third moments of the random vectors."]]></description>
<dc:subject>probability central_limit_theorem steins_method concentration_of_measure in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0649f6d1fc0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:steins_method"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/g8368h016h671270/">
    <title>Corrections to the Central Limit Theorem for Heavy-Tailed Probability Densities - Journal of Theoretical Probability, Volume 24, Number 4</title>
    <dc:date>2011-11-01T04:24:28+00:00</dc:date>
    <link>http://www.springerlink.com/content/g8368h016h671270/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Classical Edgeworth expansions provide asymptotic correction terms to the Central Limit Theorem (CLT) up to an order that depends on the number of moments available. In this paper, we provide subsequent correction terms beyond those given by a standard Edgeworth expansion in the general case of regularly varying distributions with diverging moments (beyond the second). The subsequent terms can be expressed in a simple closed form in terms of certain special functions (Dawson’s integral and parabolic cylinder functions), and there are qualitative differences depending on whether the number of moments available is even, odd, or not an integer, and whether the distributions are symmetric or not. If the increments have an even number of moments, then additional logarithmic corrections must also be incorporated in the expansion parameter. An interesting feature of our correction terms for the CLT is that they become dominant outside the central region and blend naturally with known large-deviation asymptotics when these are applied formally to the spatial scales of the CLT."

Preprint version: http://arxiv.org/abs/1103.4306]]></description>
<dc:subject>re:almost_none heavy_tails central_limit_theorem large_deviations probability in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ad2206024392/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.0963">
    <title>[1110.0963] An Empirical Process Central Limit Theorem for Multidimensional Dependent Data</title>
    <dc:date>2011-10-06T17:34:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.0963</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Let $(U_n(t))_{tinR^d}$ be the empirical process associated to an $R^d$-valued stationary process $(X_i)_{ige 0}$. We give general conditions, which only involve processes $(f(X_i))_{ige 0}$ for a restricted class of functions $f$, under which weak convergence of $(U_n(t))_{tinR^d}$ can be proved. This is particularly useful when dealing with data arising from dynamical systems or functional of Markov chains. This result improves those of [DDV09] and [DD11], where the technique was first introduced, and provides new applications."]]></description>
<dc:subject>empirical_processes stochastic_processes dynamical_systems central_limit_theorem in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:96b285d39729/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1109.0838">
    <title>[1109.0838] A central limit theorem for stationary random fields</title>
    <dc:date>2011-09-11T19:11:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1109.0838</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>ergodic_theory random_fields stochastic_processes central_limit_theorem re:almost_none in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6d224515c635/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:random_fields"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1104.2198">
    <title>[1104.2198] Central limit theorems for additive functionals of ergodic Markov diffusions processes</title>
    <dc:date>2011-04-15T21:55:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1104.2198</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>central_limit_theorem stochastic_processes markov_models re:almost_none in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2d1f84a7a08e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1103.6266">
    <title>[1103.6266] Almost Sure Invariance Principles via Martingale Approximation</title>
    <dc:date>2011-04-01T02:33:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1103.6266</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>central_limit_theorem stochastic_processes to:NB martingales re:almost_none</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5dd440b7a695/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>