Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi A short note on the median-of-means estimator (Yen-Chi Chen, 2020) 2026-04-23T16:43:42+00:00 https://faculty.washington.edu/yenchic/short_note/note_MoM.pdf cshalizi to:NB have_read statistics heavy_tails estimation empirical_processes https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ee5135168ce3/ [2501.07772] Bridging Root-$n$ and Non-standard Asymptotics: Dimension-agnostic Adaptive Inference in M-Estimation 2025-03-24T00:12:30+00:00 https://arxiv.org/abs/2501.07772 cshalizi to:NB statistics estimation kuchibhotla.arun_kmar re:HEAS via:lal.apoorva https://pinboard.in/ https://pinboard.in/u:cshalizi/b:493622f19ddd/ [2308.03296] Studying Large Language Model Generalization with Influence Functions 2025-03-10T15:14:03+00:00 https://arxiv.org/abs/2308.03296 cshalizi in_NB have_read optimization statistics computational_statistics neural_networks large_language_models_(so_called) estimation re:large_language_models_in_statistical_perspective feral_library_catalogs re:gopnikism to_teach:statistics_and_generative_ai https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3b2888a6a8c1/ Adapting to Misspecification | NBER 2025-03-08T21:25:44+00:00 https://www.nber.org/papers/w32906 cshalizi to:NB estimation misspecification statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:87581585e77d/ OSF Preprints | Quantitative Political Science Research is Greatly Underpowered 2023-05-02T20:12:17+00:00 https://osf.io/7vy2f/ cshalizi to:NB to_read political_science social_science_methodology statistics hypothesis_testing estimation re:neutral_model_of_inquiry https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c31528a09f2b/ Power of Bias in Economics Research | The Economic Journal | Oxford Academic 2023-05-02T20:10:08+00:00 https://academic.oup.com/ej/article-abstract/127/605/F236/5069452?login=false cshalizi to:NB economics econometrics statistics hypothesis_testing re:neutral_model_of_inquiry estimation have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2c2f32247eac/ [physics/0001019] Approaching the parameter estimation quality of maximum likelihood via generalized moments 2023-05-01T20:12:15+00:00 https://arxiv.org/abs/physics/0001019 cshalizi in_NB have_read likelihood estimation statistics cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 https://pinboard.in/ https://pinboard.in/u:cshalizi/b:418dde8076ff/ Phys. Rev. E 51, 3871 (1995) - Symbol sequence statistics in noisy chaotic signal reconstruction 2023-04-24T21:48:32+00:00 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.51.3871 cshalizi symbolic_dynamics dynamical_systems time_series statistical_inference_for_stochastic_processes cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 have_read re:dissertation estimation in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:04d216b60071/ Parameter-Based Asymptotics on JSTOR 2023-04-24T21:32:07+00:00 https://www.jstor.org/stable/2336834 cshalizi to_reread estimation statistics cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 likelihood statistical_inference_for_stochastic_processes re:HEAS in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5c278c6fcc6d/ Higher order asymptotics of minimax estimators for time series - Xu - - Journal of Time Series Analysis - Wiley Online Library 2022-06-28T18:35:06+00:00 https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12661?campaign=wolacceptedarticle cshalizi to:NB time_series minimax estimation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0abb8bc64211/ Predictive State Propensity Subclassification (PSPS): A causal inference method for optimal data-driven propensity score stratification – Google Research 2022-03-16T14:27:13+00:00 https://research.google/pubs/pub49197/ cshalizi to:NB causal_inference statistics estimation prediction_processes kith_and_kin goerg.georg_m. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:07155ec09ca0/ Choosing among Regularized Estimators in Empirical Economics: The Risk of Machine Learning | The Review of Economics and Statistics | MIT Press 2022-03-14T18:19:21+00:00 https://direct.mit.edu/rest/article-abstract/101/5/743/58556/Choosing-among-Regularized-Estimators-in-Empirical?redirectedFrom=fulltext cshalizi to:NB econometrics estimation statistics learning_theory to_teach:childs_garden_of_statistical_learning_theory re:HEAS downloaded cross-validation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:765356ad4562/ Inference for Ranks with Applications to Mobility across Neighborhoods and Academic Achievement across Countries by Magne Mogstad, Joseph P. Romano, Azeem Shaikh, Daniel Wilhelm :: SSRN 2021-12-07T14:28:12+00:00 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3557282 cshalizi confidence_sets ranking estimation statistics transmission_of_inequality to_teach:statistics_of_inequality_and_discrimination via:civilstat in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:462588e9a47e/ [2102.05802] Fisher Information and Mutual Information Constraints 2021-07-12T14:50:43+00:00 https://arxiv.org/abs/2102.05802 cshalizi to:NB information_theory estimation minimax re:HEAS statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:172027cea48f/ [2107.03826] Asymptotic normality of robust $M$-estimators with convex penalty 2021-07-11T05:57:15+00:00 https://arxiv.org/abs/2107.03826 cshalizi0. The asymptotic normality requires a bias correction and holds for most coordinates of the M-estimator for a large class of loss functions including the Huber loss and its smoothed versions regularized with a strongly convex penalty. "The asymptotic variance that characterizes the width of the resulting confidence intervals is estimated with data-driven quantities. This estimate of the variance adapts automatically to low (p/n→0) or high (p/n≤γ) dimensions and does not involve the proximal operators seen in previous works on asymptotic normality of M-estimators. For the Huber loss, the estimated variance has a simple expression involving an effective degrees-of-freedom as well as an effective sample size. The case of the Huber loss with Elastic-Net penalty is studied in details and a simulation study confirms the theoretical findings. The asymptotic normality results follow from Stein formulae for high-dimensional random vectors on the sphere developed in the paper which are of independent interest."]]> to:NB estimation re:HEAS statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4d4a323ea84c/ [1701.01207] Learning Semidefinite Regularizers 2021-06-10T02:08:20+00:00 https://arxiv.org/abs/1701.01207 cshalizi to:NB optimization statistics estimation color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d03e0ef626e9/ [2105.08766] The Minimax Estimator of the Average Treatment Effect, among Linear Combinations of Conditional Average Treatment Effects Estimators 2021-05-20T16:53:06+00:00 https://arxiv.org/abs/2105.08766 cshalizi to:NB causal_inference estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cd393cf47264/ [2105.04390] Statistical inference for continuous-time locally stationary processes using stationary approximations 2021-05-12T18:16:26+00:00 https://arxiv.org/abs/2105.04390 cshalizi to:NB estimation time_series statistics non-stationarity statistical_inference_for_stochastic_processes re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:df61a132f071/ The Rate-Distortion Risk in Estimation From Compressed Data | IEEE Journals & Magazine | IEEE Xplore 2021-04-23T03:04:08+00:00 https://ieeexplore.ieee.org/document/9387338 cshalizi to:NB information_theory estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7b324e2b2317/ A Review of Empirical Likelihood | Annual Review of Statistics and Its Application 2021-04-15T14:57:59+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-040720-024710 cshalizi empirical_likelihood likelihood hypothesis_testing estimation statistics lazar.nicole in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9fb214584349/ Combining Information Across Diverse Sources: The II‐CC‐FF Paradigm - Cunen - - Scandinavian Journal of Statistics - Wiley Online Library 2021-04-14T14:43:00+00:00 https://onlinelibrary.wiley.com/doi/10.1111/sjos.12530?af=R cshalizi to:NB statistics estimation meta-analysis confidence_sets hjort.nils_lid https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d81d256994c7/ [2104.03167] Random graphs with node and block effects: models, goodness-of-fit tests, and applications to biological networks 2021-04-12T17:06:07+00:00 https://arxiv.org/abs/2104.03167 cshalizi to:NB stochastic_block_models network_data_analysis estimation exponential_family_random_graphs hypothesis_testing algebraic_statistics goodness-of-fit https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3dc0c1b71041/ [2103.01604] Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference 2021-04-12T03:19:53+00:00 https://arxiv.org/abs/2103.01604 cshalizi to:NB statistics estimation time_series long-range_dependence non-stationarity https://pinboard.in/ https://pinboard.in/u:cshalizi/b:736ebd960279/ [2104.04416] Concentration study of M-estimators using the influence function 2021-04-12T02:18:49+00:00 https://arxiv.org/abs/2104.04416 cshalizi to:NB estimation heavy_tails statistics learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d69a7cca59f9/ [2012.15678] On Gaussian Approximation for M-Estimator 2021-01-03T20:05:42+00:00 https://arxiv.org/abs/2012.15678 cshalizi to:NB central_limit_theorem estimation statistics empirical_processes re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:580f8c5bf237/ Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types | Information and Inference: A Journal of the IMA | Oxford Academic 2020-12-24T02:08:19+00:00 https://doi.org/10.1093/imaiai/iaz025 cshalizi to:NB concentration_of_measure information_theory estimation large_deviations https://pinboard.in/ https://pinboard.in/u:cshalizi/b:de268154829f/ Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types | Information and Inference: A Journal of the IMA | Oxford Academic 2020-12-24T02:08:06+00:00 https://academic.oup.com/imaiai/article-abstract/9/4/813/5627733 cshalizi to:NB concentration_of_measure information_theory estimation large_deviations https://pinboard.in/ https://pinboard.in/u:cshalizi/b:82e00f75769c/ [2012.09874] Simple and statistically sound strategies for analysing physical theories 2020-12-21T04:37:17+00:00 https://arxiv.org/abs/2012.09874 cshalizi to:NB statistics estimation meta-analysis physics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ae0ca7299e90/ Distance-Based Statistical Inference | Annual Review of Statistics and Its Application 2020-12-18T18:49:05+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031219-041228 cshalizi to:NB statistics estimation re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5bb05e1ad2e5/ [2012.09422] The Variational Method of Moments 2020-12-18T10:34:37+00:00 https://arxiv.org/abs/2012.09422 cshalizi to:NB estimation re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:361066f8a023/ [2012.07167] Pseudo-likelihood-based $M$-estimation of random graphs with dependent edges and parameter vectors of increasing dimension 2020-12-15T13:02:25+00:00 https://arxiv.org/abs/2012.07167 cshalizi to:NB estimation likelihood network_data_analysis statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fd6e1a018f40/ [2011.14999] An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions? 2020-12-03T16:13:23+00:00 https://arxiv.org/abs/2011.14999 cshalizi to:NB statistics robustness estimation to_read linear_regression to_teach:linear_models to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bc72bd101639/ [1810.06838] Finite-sample analysis of M-estimators using self-concordance 2020-12-02T15:58:13+00:00 https://arxiv.org/abs/1810.06838 cshalizi to:NB estimation statistics re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e17d20d51133/ [2011.14762] Testing for Uniqueness of Estimators 2020-12-02T01:49:27+00:00 https://arxiv.org/abs/2011.14762 cshalizi to:NB statistics optimization estimation re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b0af4daf0a0e/ [2011.10240] Reconstruct Kaplan--Meier Estimator as M-estimator and Its Confidence Band 2020-11-23T17:44:03+00:00 https://arxiv.org/abs/2011.10240 cshalizi to:NB survival_analysis estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bc9164ed9839/ Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data: Journal of the American Statistical Association: Vol 0, No 0 2020-11-20T15:34:26+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1773832 cshalizi to:NB computational_statistics estimation statistics random_projections https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9d6a370bded6/ Chen , Lee , Tong , Zhang : Statistical inference for model parameters in stochastic gradient descent 2020-11-19T04:42:45+00:00 https://projecteuclid.org/euclid.aos/1581930134 cshalizi stochastic_gradient_descent optimization statistics estimation in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6b5200a14eac/ Biau , Cadre , Sangnier , Tanielian : Some theoretical properties of GANS 2020-11-18T21:50:42+00:00 https://projecteuclid.org/euclid.aos/1594972829 cshalizi to:NB statistics estimation your_favorite_deep_neural_network_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c35ca832e623/ [1710.08511] An Expectation Maximization Framework for Yule-Simon Preferential Attachment Models 2020-11-18T17:18:09+00:00 https://arxiv.org/abs/1710.08511 cshalizi to:NB estimation em_algorithm heavy_tails statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b20d7aa0a1ee/ [2010.13687] A General Approach for Simulation-based Bias Correction in High Dimensional Settings 2020-11-18T17:16:05+00:00 https://arxiv.org/abs/2010.13687 cshalizi to:NB simulation estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e3587dcd66c8/ Systematic review of the use of “magnitude-based inference” in sports science and medicine 2020-07-13T16:43:55+00:00 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235318 cshalizi to:NB have_read bad_data_analysis statistics why_oh_why_cant_we_have_a_better_academic_publishing_system hypothesis_testing estimation trapped_in_plutos_republic https://pinboard.in/ https://pinboard.in/u:cshalizi/b:47bc87dbe9e7/ Asymptotically Optimal Regularization in Smooth Parametric Models 2020-05-16T18:08:57+00:00 http://papers.nips.cc/paper/3693-asymptotically-optimal-regularization-in-smooth-parametric-models cshalizi to:NB statistics estimation learning_theory to_read re:HEAS to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:453fbf1714bd/ Empirical Likelihood Methods in Econometrics: Theory and Practice by Yuichi Kitamura :: SSRN 2020-05-16T18:01:29+00:00 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=917901 cshalizi to:NB statistics estimation hypothesis_testing likelihood empirical_likelihood large_deviations re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d0c1ac40de03/ [1911.01483] Statistical Inference for Model Parameters in Stochastic Gradient Descent via Batch Means 2019-11-09T23:33:31+00:00 https://arxiv.org/abs/1911.01483 cshalizi to:NB optimization estimation statistics re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a44533e10261/ [1910.11540] Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection 2019-10-29T14:24:18+00:00 https://arxiv.org/abs/1910.11540 cshalizi to:NB minimum_description_length statistics estimation prediction change-point_problem https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5c50517054ec/ [1602.02201] The Rate-Distortion Risk in Estimation from Compressed Data 2019-10-25T14:46:27+00:00 https://arxiv.org/abs/1602.02201 cshalizi to:NB estimation information_theory compression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:949016946f05/ How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions | The Review of Economics and Statistics | MIT Press Journals 2019-10-24T15:01:31+00:00 https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00795 cshalizi to:NB economics econometrics statistics estimation shrinkage https://pinboard.in/ https://pinboard.in/u:cshalizi/b:384ae1da5083/ [1910.08390] Finite sample deviation and variance bounds for first order autoregressive processes 2019-10-22T13:52:31+00:00 https://arxiv.org/abs/1910.08390 cshalizi to:NB statistics deviation_inequalities time_series estimation to_teach:data_over_space_and_time https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0fbea03e7f65/ [1910.09457] Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction 2019-10-22T13:46:12+00:00 https://arxiv.org/abs/1910.09457 cshalizi prediction estimation uncertainty_for_neural_networks statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c45bebcfc6ae/ [1910.07185] Identifying relationships between cognitive processes across tasks, contexts, and time 2019-10-17T14:09:56+00:00 https://arxiv.org/abs/1910.07185 cshalizi to:NB cognitive_science psychometrics statistics estimation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a6e899fb508c/ [1703.09965] Estimable group effects for strongly correlated variables in linear models 2019-10-17T14:08:26+00:00 https://arxiv.org/abs/1703.09965 cshalizi to:NB linear_regression estimation statistics to_teach:linear_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cb8ae8464bab/ [1904.03313] On the computational tractability of statistical estimation on amenable graphs 2019-09-26T18:35:37+00:00 https://arxiv.org/abs/1904.03313 cshalizi to:NB network_data_analysis estimation statistics theoretical_computer_science minimax graph_limits https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e7979f915817/ [1909.10828] Double-estimation-friendly inference for high-dimensional misspecified models 2019-09-26T18:02:05+00:00 https://arxiv.org/abs/1909.10828 cshalizi to:NB misspecification estimation high-dimensional_statistics buhlmann.peter statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ae504d441d08/ [1909.09336] Applications of Generalized Maximum Likelihood Estimators to stratified sampling and post-stratification with many unobserved strata 2019-09-23T14:18:45+00:00 https://arxiv.org/abs/1909.09336 cshalizi to:NB missing_data statistics estimation surveys https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ce530ec2b34b/ [1909.05582] A taxonomy of estimator consistency on discrete estimation problems 2019-09-13T13:03:59+00:00 https://arxiv.org/abs/1909.05582 cshalizi to:NB statistics estimation model_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:34bd01754668/ [1702.08109] Variational Analysis of Constrained M-Estimators 2019-09-12T16:24:45+00:00 https://arxiv.org/abs/1702.08109 cshalizi to:NB estimation empirical_processes statistics nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e5d16319e505/ [1909.00579] Asymptotic linear expansion of regularized M-estimators 2019-09-04T15:19:57+00:00 https://arxiv.org/abs/1909.00579 cshalizi to:NB estimation statistics re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cce2a10774d2/ [1607.06163] Indirect Inference With(Out) Constraints 2019-08-21T13:13:50+00:00 https://arxiv.org/abs/1607.06163 cshalizi0, such as, when I-I is based on GARCH auxiliary models. In these settings we propose a novel I-I approach that uses appropriately modified unconstrained auxiliary statistics, which are simple to compute and always exists. We state the relevant asymptotic theory for this I-I approach without constraints and show that it can be reinterpreted as a standard implementation of I-I through a properly modified binding function. Several examples that have featured in the literature illustrate our approach."]]> to:NB indirect_inference estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b22c43eccb9f/ [1302.0890] Local Log-linear Models for Capture-Recapture 2019-08-20T14:10:03+00:00 https://arxiv.org/abs/1302.0890 cshalizi to:NB have_read surveys smoothing statistics estimation kurtz.zachary kith_and_kin https://pinboard.in/ https://pinboard.in/u:cshalizi/b:509f22ef5f94/ [1901.00555] An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation 2019-08-19T13:25:06+00:00 https://arxiv.org/abs/1901.00555 cshalizi information_theory minimax statistics estimation in_NB have_read re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7f6e296ba272/ [1908.04748] Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching 2019-08-14T19:19:35+00:00 https://arxiv.org/abs/1908.04748 cshalizi to:NB causal_inference estimation matching statistics kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c8e1a84e8445/ Non-Standard Parametric Statistical Inference - Russell Cheng - Oxford University Press 2019-08-05T18:37:11+00:00 https://global.oup.com/academic/product/non-standard-parametric-statistical-inference-9780198505044?cc=us&lang=en# cshalizi to:NB bootstrap estimation hypothesis_testing statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3db28c9164d7/ [1908.00598] Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation 2019-08-05T12:49:28+00:00 https://arxiv.org/abs/1908.00598 cshalizi neural_networks estimation statistics propagation_of_error everything_old_is_new_again uncertainty_for_neural_networks have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8a98269b714d/ [1908.00310] Maximum likelihood estimation of power-law degree distributions using friendship paradox based sampling 2019-08-02T13:19:22+00:00 https://arxiv.org/abs/1908.00310 cshalizi to:NB network_data_analysis heavy_tails estimation statistics color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:58d86ca6973c/ [1510.00551] Investigation of Parameter Uncertainty in Clustering Using a Gaussian Mixture Model Via Jackknife, Bootstrap and Weighted Likelihood Bootstrap 2019-07-24T13:59:14+00:00 https://arxiv.org/abs/1510.00551 cshalizi to:NB statistics estimation mixture_models confidence_sets https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c84a3e095cf8/ [1906.08283] Minimum Stein Discrepancy Estimators 2019-06-23T17:35:41+00:00 https://arxiv.org/abs/1906.08283 cshalizi to:NB statistics estimation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:935c44965417/ [1712.07248] Towards a General Large Sample Theory for Regularized Estimators 2019-06-21T15:43:45+00:00 https://arxiv.org/abs/1712.07248 cshalizi statistics estimation optimization to_read in_NB re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:54557552758c/ [1906.05944] Statistical Inference for Generative Models with Maximum Mean Discrepancy 2019-06-19T15:50:34+00:00 https://arxiv.org/abs/1906.05944 cshalizi simulation statistics estimation hilbert_space simulation-based_inference in_NB to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:148889e70eec/ [1805.07454] Fisher Efficient Inference of Intractable Models 2019-05-29T21:13:23+00:00 https://arxiv.org/abs/1805.07454 cshalizi to:NB likelihood estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d647f388c259/ [1903.06576] A nonasymptotic law of iterated logarithm for general M-estimators 2019-05-27T15:06:29+00:00 https://arxiv.org/abs/1903.06576 cshalizi to:NB estimation deviation_inequalities statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8e1762700ea4/