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  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (dvse)</title>
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    <description>recent bookmarks from dvse</description>
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      <rdf:Seq>	<rdf:li rdf:resource="http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/publications/StatisticalLearningTheory.pdf"/>
	<rdf:li rdf:resource="http://homes.di.unimi.it/~cesabian/Pubblicazioni/banditSurvey.pdf"/>
	<rdf:li rdf:resource="http://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/pdf/1301.3578v2.pdf"/>
	<rdf:li rdf:resource="http://www.statslab.cam.ac.uk/~rrw1/stats/Sa4.pdf"/>
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	<rdf:li rdf:resource="http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf"/>
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	<rdf:li rdf:resource="https://www.psych.umn.edu/faculty/waller/classes/mult12/readings/sweep1979.pdf"/>
	<rdf:li rdf:resource="http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf"/>
	<rdf:li rdf:resource="http://www.casact.org/pubs/proceed/proceed99/99317.pdf"/>
	<rdf:li rdf:resource="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm"/>
	<rdf:li rdf:resource="http://www.admb-project.org/"/>
	<rdf:li rdf:resource="http://www.seas.harvard.edu/courses/cs281/"/>
	<rdf:li rdf:resource="http://www.cs.mcgill.ca/~vkules/bandits.pdf"/>
	<rdf:li rdf:resource="http://stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf"/>
	<rdf:li rdf:resource="http://www.dme.im.ufrj.br/arquivos/publicacoes/arquivo249.pdf"/>
	<rdf:li rdf:resource="http://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20481.pdf"/>
	<rdf:li rdf:resource="http://rsta.royalsocietypublishing.org/content/222/594-604/309.full.pdf"/>
	<rdf:li rdf:resource="http://www.stat.umn.edu/geyer/lecam/"/>
	<rdf:li rdf:resource="http://www.international.ucla.edu/media/files/Leamer_article.pdf"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.lnms/1249305323"/>
	<rdf:li rdf:resource="http://www.jstor.org/stable/2281561"/>
	<rdf:li rdf:resource="http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568"/>
      </rdf:Seq>
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  </channel><item rdf:about="http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/publications/StatisticalLearningTheory.pdf">
    <title>Statistical Learning Theory: Models, Concepts, and Results</title>
    <dc:date>2013-05-10T04:06:01+00:00</dc:date>
    <link>http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/publications/StatisticalLearningTheory.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Statistical learning theory provides the theoretical basis for many of today’s machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide popularity in the 1990s following the development of the so-called Support Vector Machine (SVM), which has become a standard tool for pattern recognition in a variety of domains ranging from computer vision to computational biology. Providing the basis of new learning algorithms, however, was not the only motivation for developing statistical learning theory. It was just as much a philosophical one, attempting to answer the question of what it is that allows us to draw valid conclusions from empirical data."]]></description>
<dc:subject>statistics machine_learning statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:f9d0b888c39d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homes.di.unimi.it/~cesabian/Pubblicazioni/banditSurvey.pdf">
    <title>Regret analysis of stochastic and nonstochastic multi-armed bandit problems</title>
    <dc:date>2013-04-06T13:05:43+00:00</dc:date>
    <link>http://homes.di.unimi.it/~cesabian/Pubblicazioni/banditSurvey.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration–exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of bandit problems dates back to the 1930s, exploration–exploitation trade-offs arise in several modern applications, such as ad placement, website optimization, and packet routing."]]></description>
<dc:subject>statistics control_theory bandits survey</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:114e04b7a708/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:control_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:bandits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:survey"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf">
    <title>Strictly Proper Scoring Rules, Prediction, and Estimation</title>
    <dc:date>2013-02-16T10:09:07+00:00</dc:date>
    <link>http://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he or she issues the probabilistic forecast F, rather than G = F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest."]]></description>
<dc:subject>statistics decision_theory estimation economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d544339a4ed7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/pdf/1301.3578v2.pdf">
    <title>Cramer-Rao Lower Bound and Information Geometry</title>
    <dc:date>2013-02-15T07:32:49+00:00</dc:date>
    <link>http://arxiv.org/pdf/1301.3578v2.pdf</link>
    <dc:creator>dvse</dc:creator><dc:subject>statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:477e3f35fc59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.statslab.cam.ac.uk/~rrw1/stats/Sa4.pdf">
    <title>Cambridge Statistics IB</title>
    <dc:date>2012-10-16T08:13:40+00:00</dc:date>
    <link>http://www.statslab.cam.ac.uk/~rrw1/stats/Sa4.pdf</link>
    <dc:creator>dvse</dc:creator><dc:subject>statistics hypothesis_testing generalized_likelihood_ratio</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:9a361b77d672/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:generalized_likelihood_ratio"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math-ph/0002049">
    <title>Classical and Quantum Probability</title>
    <dc:date>2012-08-11T15:25:00+00:00</dc:date>
    <link>http://arxiv.org/abs/math-ph/0002049</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["We survey the development of probability from 1900, starting with Bachelier's theory of speculation. Fisher information appears in the theory of estimation. We touch on Brownian motion, and the Wiener integral. The Ito calculus, and its relation to to the heat equation, is mentioned. Quantum theory is introduced as a generalisation of probability, rather than of mechanics. The weakness of attempts to describe quantum theory in terms of hidden variables is explained, by a simple proof of Bell's inequality.
Quantum versions of the Langevin equation are discussed, and the theory of continuous tensor products is used to give a possible quantum version. The quantum stochastic calculus of Barnett, Wilde and the author, as well as that of Parthasarathy and Hudson, is introduced."]]></description>
<dc:subject>statistics foundations finance quantum_mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:1ba1cc2416ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:quantum_mechanics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf">
    <title>Causal Inference in Statistics: An Overview</title>
    <dc:date>2012-08-11T14:32:48+00:00</dc:date>
    <link>http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals."]]></description>
<dc:subject>causal_inference statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:241872b3b584/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081">
    <title>On the history and use of some standard statistical models</title>
    <dc:date>2012-07-30T04:52:24+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper tries to tell the story of the general linear model, which saw the light of day 200 years ago, and the assumptions underlying it. We distinguish three principal stages (ignoring earlier more isolated instances). The model was first proposed in the context of astronomical and geodesic observations, where the main source of variation was observational error. This was the main use of the model during the 19th century.

In the 1920’s it was developed in a new direction by R.A. Fisher whose principal applications were in agriculture and biology. Finally, beginning in the 1930’s and 40’s it became an important tool for the social sciences. As new areas of applications were added, the assumptions underlying the model tended to become more questionable, and the resulting statistical techniques more prone to misuse."]]></description>
<dc:subject>statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d46068115267/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.psych.umn.edu/faculty/waller/classes/mult12/readings/sweep1979.pdf">
    <title>A Tutorial on the SWEEP Operator</title>
    <dc:date>2012-07-25T14:11:04+00:00</dc:date>
    <link>https://www.psych.umn.edu/faculty/waller/classes/mult12/readings/sweep1979.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["First, this tutorial describes the use of Gauss-Jordan elimination for least squares and continues with a description of a completely generalized sweep operator ..."]]></description>
<dc:subject>linear_algebra statistics numerical_methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:01e47d1d83cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:linear_algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:numerical_methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf">
    <title>Regression and Causation: A Critical Examination of Econometrics Textbooks</title>
    <dc:date>2012-07-13T09:35:27+00:00</dc:date>
    <link>http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This report surveys six influential econometric textbooks in terms of their math- ematical treatment of causal concepts. It highlights conceptual and notational differ- ences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that econonometric textbooks vary from complete denial to partial acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent mathematical notation that distinguishes causal from statistical concepts. This survey also provides a panoramic view of the state of causal thinking in econometric education which, to the best of our knowledge, has not been surveyed before."
]]></description>
<dc:subject>causal_inference economics econometrics regression statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:059611dcb7a1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.casact.org/pubs/proceed/proceed99/99317.pdf">
    <title>A Systematic Relationship between Minimum Bias and Generalized Linear Models</title>
    <dc:date>2012-07-12T05:35:22+00:00</dc:date>
    <link>http://www.casact.org/pubs/proceed/proceed99/99317.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Those were the days when insurance was one of the most complex applications of statistics around.]]></description>
<dc:subject>actuarial statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:130311a6a41c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:actuarial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm">
    <title>MIT 6.262 - Discrete Stochastic Processes</title>
    <dc:date>2012-07-04T07:59:20+00:00</dc:date>
    <link>http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Course by Robert Gallager]]></description>
<dc:subject>statistics stochastic_processes video_lectures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:1fed794ec188/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:video_lectures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.admb-project.org/">
    <title>ADMB Project</title>
    <dc:date>2012-07-02T05:29:02+00:00</dc:date>
    <link>http://www.admb-project.org/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["AD Model Builder, or ADMB, the most powerful software package for the development of state-of-the-art nonlinear models, can now be freely downloaded for Windows, Linux, MacOS, and Sun/SPARC "]]></description>
<dc:subject>optimization statistics random_effects software</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:5ff9ce6f439a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:random_effects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:software"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.seas.harvard.edu/courses/cs281/">
    <title>CS281: Advanced Machine Learning (Harvard)</title>
    <dc:date>2012-07-01T09:34:26+00:00</dc:date>
    <link>http://www.seas.harvard.edu/courses/cs281/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Many links to video lectures.]]></description>
<dc:subject>machine_learning statistics video_lectures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:015b6413d563/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:video_lectures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.mcgill.ca/~vkules/bandits.pdf">
    <title>Algorithms for the multi-armed bandit problems</title>
    <dc:date>2012-05-30T00:26:48+00:00</dc:date>
    <link>http://www.cs.mcgill.ca/~vkules/bandits.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["The stochastic multi-armed bandit problem is an important model for studying the exploration-exploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical conrmation of their eectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms."]]></description>
<dc:subject>stochastic_control statistics bandits</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:72710c8205cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:bandits"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf">
    <title>Statistical learning theory and sequential prediction</title>
    <dc:date>2012-05-27T07:20:02+00:00</dc:date>
    <link>http://stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[UPenn STAT928 notes]]></description>
<dc:subject>statistics convex_optimization asymptotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:b6f653dda075/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:asymptotics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.dme.im.ufrj.br/arquivos/publicacoes/arquivo249.pdf">
    <title>Generalized Linear Models with Random Eﬀects in the Two-Parameter Exponential Family</title>
    <dc:date>2012-05-11T12:01:00+00:00</dc:date>
    <link>http://www.dme.im.ufrj.br/arquivos/publicacoes/arquivo249.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["In this paper we develop a new class of double generalized linear models, introducing a random eﬀect component in the link function describing the linear predictor related to the precision parameter. This is a useful procedure to take into account extra variability and also to make the model more robust. The Bayesian paradigm is adopted to make inference in this class of models. Samples of the joint posterior distribution are draw using standard MCMC procedures. Finally, we illustrate this algorithm by considering simulated and real data sets."]]></description>
<dc:subject>exponential_family statistics convexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:0fb60fa48605/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20481.pdf">
    <title>Overdispersed generalized linear models</title>
    <dc:date>2012-05-10T03:44:23+00:00</dc:date>
    <link>http://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20481.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Generalized linear models have become a. standard class of models for data analysts. However in some applications, heterogeneity in samples is too great to be explained by the simple variance function implicit in such models. Utilizing a. two parameter exponential family which is overdispersed relative to a specified one parameter exponential family enables the  creation of classes of overdispersed generalized linear models (OGLM’s) which are analytically attractive."]]></description>
<dc:subject>statistics exponential_family</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:4a935b3835e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rsta.royalsocietypublishing.org/content/222/594-604/309.full.pdf">
    <title>On the mathematical foundations of theoretical statistics</title>
    <dc:date>2012-04-01T13:07:29+00:00</dc:date>
    <link>http://rsta.royalsocietypublishing.org/content/222/594-604/309.full.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[R.A Fisher's classical paper outlining the maximum likelihood principle and the notions of sufficiency, efficiency and consistency.]]></description>
<dc:subject>statistics foundations estimation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:4834fa210d16/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.umn.edu/geyer/lecam/">
    <title>Le Cam Made Simple: No-N Asymptotics</title>
    <dc:date>2012-02-15T00:48:44+00:00</dc:date>
    <link>http://www.stat.umn.edu/geyer/lecam/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["If the log likelihood is approximately quadratic with constant Hessian, then the maximum likelihood estimator (MLE) is approximately normally distributed. No other assumptions are required. We do not need independent and identically distributed data. We do not need the law of large numbers (LLN) or the central limit theorem (CLT). We do not need sample size going to infinity or anything going to infinity.

The theory presented here is a combination of Le Cam style involving local asymptotic normality (LAN) and local asymptotic mixed normality (LAMN) and Cramér style involving derivatives and Fisher information. The main tool is convergence in law of the log likelihood function and its derivatives considered as random elements of a Polish space of continuous functions with the metric of uniform convergence on compact sets. We obtain results for both one-step-Newton estimators and Newton-iterated-to-convergence estimators."]]></description>
<dc:subject>statistics estimation asymptotics via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:1c3bcdeeaf3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:asymptotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.international.ucla.edu/media/files/Leamer_article.pdf">
    <title>Let's take the Con out of Econometrics (Leamer)</title>
    <dc:date>2012-02-13T13:23:56+00:00</dc:date>
    <link>http://www.international.ucla.edu/media/files/Leamer_article.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[What to do when "experiments" can not be controlled (not much, but indoctrination helps) - predates current IV methodology?]]></description>
<dc:subject>econometrics foundations statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:ab99b9af5b7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.lnms/1249305323">
    <title>Huber: On the Non-Optimality of Optimal Procedures</title>
    <dc:date>2012-02-13T12:35:41+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.lnms/1249305323</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper discusses some subtle, and largely overlooked, differences between conceptual and mathematical optimization goals in statistics, and illustrates them by examples."
]]></description>
<dc:subject>statistics robust_statistics optimization foundations via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:710548abe3f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:robust_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/stable/2281561">
    <title>A Method of Handling Curvilinear Correlation for Any Number of Variables (Ezekiel, 1924)</title>
    <dc:date>2012-02-13T11:08:43+00:00</dc:date>
    <link>http://www.jstor.org/stable/2281561</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Additive regression models from 1924, together with an algorithm which  looks even more labour intensive than Whittaker graduation!]]></description>
<dc:subject>regression additive_models statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:fd58faecbfa1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568">
    <title>On a New Method of Graduation</title>
    <dc:date>2012-02-13T11:06:57+00:00</dc:date>
    <link>http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Whittaker introduces 1D smoothing in 1922, complete with the Bayesian derivation.   There is an earlier German paper with a similar model.]]></description>
<dc:subject>actuarial splines smoothing regression statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:1c749ed847aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:actuarial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:splines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>