Gelman, Andrew - Department of Statistics, Columbia University

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Gelman, Andrew - Department of Statistics, Columbia University

Bayesian Computation for Parametric Models of Heteroscedasticity in the Linear Model
Way of life Extra material
La philosophie baysienne conventionnelle L'analyse des donnes baysienne
WHAT IS THE PROBABILITY YOUR VOTE WILL MAKE A DIFFERENCE? ANDREW GELMAN, NATE SILVER and AARON EDLIN
Markov Chain Monte Carlo Methods in Biostatistics Andrew Gelman
The Annals of Applied Statistics 2008, Vol. 2, No. 4, 13601383
Poststrati cation Without Population Level Information on the Poststratifying Variable,
IMPROVING ON PROBABILITY WEIGHTING FOR HOUSEHOLD SIZE
Treatment effects in before-after data1
Book Reviews Berk, R. (2004). Regression Analysis: A Constructive Critique.
One vote, many Mexicos: Income and vote choice in the 1994, 2000, and 2006 presidential elections
Inference and Monitoring Convergence
A Catch-22 in Assigning Primary Delegates Howard Wainer
Submitted to the Annals of Statistics ON THE STATIONARY DISTRIBUTION OF ITERATIVE
Parameterization and Bayesian Modeling Andrew Gelman
A Broken System: The Persistent Patterns of Reversals of Death
Issues specific to correlations in medical imaging What is the multiple comparisons problem?
The Playing Field Shifts: Predicting the Seats-Votes Curve in the 2008
The Boxer, the Wrestler, and the Coin Flip: A Paradox of Robust Bayesian Inference and Belief Functions
Solutions to some exercises from Bayesian Data Analysis, second edition, by Gelman, Carlin, Stern, and Rubin
Extension of the Isobolographic Approach to Interactions Studies between More than Two Drugs: Illustration
Curriculum Vitae Andrew Gelman
This book is intended to have three roles and to serve three associated audi-ences: an introductory text on Bayesian inference starting from first principles,
Subject index adding parameters to a model, 177178
List of models Discrete conditional probabilities 9, 552
Data Analysis Using Regression and Multilevel/Hierarchical Models
Subject index 1.6, factor for converting from logit to
Going Beyond the Book: Towards Critical Reading in Statistics Teaching
Inference from simulations and monitoring convergence1 Andrew Gelman and Kenneth Shirley2
JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/
Breaking down the 2008 vote1 Andrew Gelman2
8 VOL. 23, NO. 3, 2010 raditionally, the Republican Party has
258 MATHEMATICS MAGAZINE What Do We Know at 7 PM on Election Night?
Volume 8, Issue 1 2010 Article 8 THE POLITICS OF HEALTH CARE REFORM
Volume 1, Issue 1 2010 Article 3 Statistics, Politics, and Policy
doi:10.1093/pan/mpq002 Bayesian Combination of State Polls and
A reprint from American Scientistthe magazine of Sigma Xi, The Scientific Research Society
David Madigan & Andrew Gelman Columbia University
C H A P T E R N I N E T E E N Beautiful Political Data
Splitting a Predictor at the Upper Quarter or Third and the Lower Quarter or Third
Discussion of the paper "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by H. Rue, S. Martino and N. Chopin, for the Journal
submitted to Bayesian Analysis (2008) 0, Number 0, pp. 15 Objections to Bayesian statistics
submitted to Bayesian Analysis (2008) 0, Number 0, pp. 111 Response to discussions of "Objections to
2008 Royal Statistical Society 00359254/08/57273 Appl. Statist. (2008)
The Annals of Applied Statistics 2008, Vol. 2, No. 2, 536549
STATISTICS IN MEDICINE Statist. Med. 2008; 27:28652873
This article was downloaded by:[Centers for Disease Control and Prevention] On: 12 February 2008
Law, Probability and Risk Page 1 of 12 doi:10.1093/lpr/mgm034 BOOK REVIEW
Bayesian Hierarchical Classes Analysis Iwin Leenen
Journal of Theoretical Biology 245 (2007) 597599 Letter to Editor
Statistical Science 2007, Vol. 22, No. 2, 184188
AVERAGE PREDICTIVE COMPARISONS FOR MODELS
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models
Bayesian Analysis (2006) 1, Number 3, pp. 515533 Prior distributions for variance parameters in
Multilevel (Hierarchical) Modeling: What It Can and Cannot Do
JSS Journal of Statistical Software 1 2005, Volume 12, Issue 3. http://www.jstatsoft.org/
2005 Royal Statistical Society 00359254/05/54781 Appl. Statist. (2005)
The Annals of Statistics 2005, Vol. 33, No. 1, 153
Advance Access publication March 1, 2005 Political Analysis (2005) 13:171187 doi:10.1093/pan/mpi010
Biometrics 61, 7485 Multiple Imputation for Model Checking: Completed-Data
Reliability of a Commercial Kit To Test Groundwater for Arsenic in
Biometrics 60, 954964 December 2004
55,000 residents desperately need your help! Andrew Gelman
Statistica Sinica 13(2003), 423-442 A BAYESIAN APPROACH TO THE SELECTION AND
Statistical Science 2002, Vol. 17, No. 4, 420435
Prior distribution Andrew Gelman
Teacher's Corner You Can Load a Die, But You Can't Bias a Coin
Journalof EducationialanidBehaviorcal Statistics Sunnmer 2001, Vol. 26, No. 2, pp. 153-179
Analysis of Large-Scale Social Surveys1 Elaine Zanutto2
Poststrati cation and weighting adjustments Andrew Gelmany
Comment on the article by Stephens and Donnelly Stephen Brooks and Andrew Gelman
Printed in Great Britain Biostatistics (2000), 1, 1, pp. 2734
Statistical Science 1999, Vol. 14, No. 3, 305337
Not Asked and Not Answered: Multiple Imputation for Multiple Surveys Andrew Gelman; Gary King; Chuanhai Liu
Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Poststrati cation Into Many Categories Using Hierarchical Logistic Regression
This is page 1 Printer: Opaque this
Iterative and Non-iterative Simulation Algorithms Andrew Gelman
Submitted to the Annals of Statistics arXiv: math.PR/0000000
Avoiding Boundary Estimates in Linear Mixed Models Through Weakly Informative Priors
Deep Interactions with MRP Presidential Turnout and Voting Patterns Among Small Electoral Subgroups
Philosophy and the practice of Bayesian statistics Andrew Gelman
What Does "Do Campaigns Matter?" Mean? Joseph Bafumi
Hierarchical modeling and prior information: an example from
Infovis vs. Statistical Graphics Andrew Gelman and Antony Unwin
Of beauty, sex, and power: Statistical challenges in estimating small effects
Weakly informative priors Interactions in before-after studies
Les Riches, et les Pauvres, et le Gographie Explications et Implications
Posterior Predictive Checking and Generalized Graphical Models
Some computational and modeling issues for hierarchical models
Logistic regression Weakly informative priors
Arsenic in Bangladesh Decision analysis
Introduction Individual, county, and state-level analyses
Coalitions and political instability Mathematical and statistical models of voting power
Social and political polarization Background: how many people do you know?
Effectiveness Way of life
Pre-election polls and forecasting Partisan bias in the electoral college?
Background: how many people do you know? A model of overdispersion in social networks
Bayesian data analysis: what it is and what it is not
(1) Information aggregation--a statistical perspective
Solutions to some exercises from Bayesian Data Analysis, first edition by Gelman, Carlin, Stern, and Rubin
Stat Comput (2007) 17: 235244 DOI 10.1007/s11222-007-9020-4
STATISTICS IN MEDICINE Statist. Med. 2000; 19:23092320
Parameterization and Bayesian Modeling Andrew Gelman
Statistical Science 2010, Vol. 25, No. 2, 162165
Geography, Income, and Voters Explanations and Implications
Some Issues in Monitoring Convergence of Iterative Simulations Stephen Brooks and Andrew Gelman
What is the multiple comparisons problem? Why don't I (usually) care?
Infovis and Statistical Graphics: Different Goals, Different Looks1 Andrew Gelman2
Fully Bayesian Computing Jouni Kerman
Culture wars, voting, and polarization: divisions and unities
Games and Economic Behavior 57 (2006) 123154 www.elsevier.com/locate/geb
Advance Access publication August 3, 2005 Political Analysis (2005) 13:459461 doi:10.1093/pan/mpi032
"Not only defended but also applied": A look back at Feller's take on Bayesian
Statistical Science 2011, Vol. 26, No. 1, 1011
Economists' Voice www.bepress.com/ev October, 2008 The Berkeley Electronic Press s the election approaches, please
Fitting Multilevel Models When Predictors and Group Effects Correlate
Vol. 28, No. 2, MarchApril 2009, p. 226 issn 0732-2399 eissn 1526-548X 09 2802 0226
Statistica Sinica 20 (2010), 000-000 ADAPTIVELY SCALING THE METROPOLIS ALGORITHM
Weakly informative priors Static sensitivity analysis
Culture wars, voting, and polarization: divisions and unities in modern American politics
Predicting and Dissecting the Seats-Votes Curve in the 2006 U.S. House Election
Bayesiaanse Variantieanalyse Andrew Gelman
Examples, demos, drills, projects Getting it to happen
Interactions in before-after studies Interactions in regressions
Hierarchical modeling: a unifying framework and some open questions
Posterior distribution Andrew Gelman
Statistica Sinica 6(1996), 733-807 POSTERIOR PREDICTIVE ASSESSMENT OF MODEL
Not Asked and Not Answered: Multiple Imputation for Multiple Surveys: Andrew Gelman; Gary King; Chuanhai Liu
Statistical Science 2005, Vol. 20, No. 4, 380381
Teaching Statistics. Volume 24, Number 3, Autumn 2002 93 .Blackwell Science LtdOxford, UKTESTTeaching Statistics0141-982X Teaching Statistics Trust 200220022431000Original Article
http://smr.sagepub.com Sociological Methods
Summary. Modern Bayesian inference is highly computational but commonly pro-ceeds without reference to modern developments in statistical graphics. This should
Modeling Di erential Nonresponse in Sample Surveys Thomas C. Little, Morgan Stanley Dean Witter, New York, NY
Using Redundant Parameterizations to Fit Hierarchical Models
Weighted Classical Variogram Estimation for Data With Clustering
Analysis of variance Andrew Gelman
Spatial variability of arsenic in 6000 tube wells in a 25 km2 of Bangladesh
Structured data and multilevel models Understanding multilevel models and variance components
Quarterly Journal of Political Science, 2007, 2: 345367 Rich State, Poor State, Red State,
State-level opinions from national polls Where do weights come from?
Where do weights come from? Inference using survey weights and poststratification
Validation of Software for Bayesian Models Using Posterior Quantiles
Comment on the article by Hodges Andrew Gelman, Department of Statistics, Columbia University, New York, NY 10027
The Stata Journal (2009) 9, Number 2, pp. 315320
Do we need an integrated Bayesian/likelihood Andrew Gelman
Example of computation in R and Bugs We illustrate some of the practical issues of simulation by fitting a single This
An Analysis of the New York City Police Department's "Stop-and-Frisk" Policy in the Context
Statistical Science 2007, Vol. 22, No. 3, 349352
Philosophy and the practice of Bayesian statistics in the social sciences1 Andrew Gelman, Dept of Statistics and Dept of Political Science, Columbia University
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Estimating the probability of events that have never occurred: When is your ...
B.J.Pol.S. 34, 657674 Copyright 2004 Cambridge University Press DOI: 10.1017/S0007123404000237 Printed in the United Kingdom
Economic Disparities and Life Satisfaction in European Regions
The question The data and model
Correlations and Multiple Comparisons in Functional Imaging a Statistical Perspective
"How many zombies do you know?" Using indirect survey methods to measure alien attacks and outbreaks
Regression Modeling and Meta-Analysis for Decision Making: A Cost-Benefit Analysis
Why we (usually) don't have to worry about multiple comparisons
Generalizing the probability matrix decomposition model: an example of Bayesian model checking and model expansion
Introduction Individual, county, and state-level analyses
Scaling regression inputs by dividing by 2 sd Prior distribution for logistic regression
Segregation in Social Networks based on Acquaintanceship and Trust
Economics and voter irrationality1 Andrew Gelman2
Statistical Science 2009, Vol. 24, No. 2, 176178
Statistical Science 2007, Vol. 22, No. 2, 153164
Contributions to Economic Analysis Volume 2, Issue 1 2003 Article 13
NBER WORKING PAPER SERIES PROTECTING MINORITIES IN BINARY ELECTIONS
Culture wars, voting, and polarization: divisions and unities
An Article Submitted to Manuscript 1355
Some thoughts on the BUGS package for Bayesian analysis1 Andrew Gelman, Dept of Statistics and Dept of Political Science, Columbia University
Biometrics 60, 407417 Bayesian Analysis of Serial Dilution Assays
The Difference Between "Significant" and "Not Significant" is not Itself Statistically Significant
Arch Toxicol (1996) 70: 347--355 Springer-Verlag 1996 ORIGINAL INVESTIGATION
Parameterization and Bayesian Modeling Andrew GELMAN
Output Assessment for Monte Carlo Simulations via the Score Statistic
Causality and Statistical Learning1 Andrew Gelman2
Culture wars, voting, and polarization: divisions and unities
Model Checking and Model (chapter for Gilks, Richardson, and
Graphical models and Bayesian data analysis Computing like a Bayesian
This article was originally published in a journal published by Elsevier, and the attached copy is provided by Elsevier for the
Review of "The Search for Certainty" by Krzysztof Burdzy1 Andrew Gelman2
General Methods for Monitoring Convergence of Iterative Simulations Stephen P. Brooks; Andrew Gelman
Estimating Incumbency Advantage and Its Variation, as an Example of a BeforeAfter Study
Weakly informative priors Interactions in before-after studies
Social and political polarization Background: how many people do you know?
Teaching Statistics. Volume 25, Number 3, Autumn 2003 85 BOOK REVIEW
Statistical Methodology 7 (2010) 187209 Contents lists available at ScienceDirect
STATISTICS IN MEDICINE Statist. Med. 18, 32213234 (1999)
What is the multiple comparisons problem? Why don't I (usually) care?
State-Level Opinions from National Surveys: Poststratification using Multilevel Logistic Regression
Political representation Trench warfare
A Simple Scheme to Improve the Efficiency of Referenda Alessandra Casella, Andrew Gelman
Improving the Presentation of
Sampling for Bayesian computation with large datasets Zaiying Huang
Political Analysis (2004) 12:375385 doi:10.1093/pan/mph024
Bayesian probabilistic extensions of a determin-istic classi cation model
List of models xv List of examples xvii
Induction and deduction in Bayesian data analysis1 Andrew Gelman, Dept of Statistics and Dept of Political Science, Columbia University
Experimental reasoning in social science1 Andrew Gelman2
A new look at death penalty public opinion across time by state By Kenny Shirley and Andrew Gelman
A Note on Bivariate Distributions That Are Conditionally Normal
The No-U-Turn Sampler The No-U-Turn Sampler: Adaptively Setting Path Lengths
"Not only defended but also applied": The perceived absurdity of Bayesian inference
Understanding persuasion and activation in presidential campaigns: The random walk and mean-reversion
Parameterization and Bayesian Modeling Andrew Gelman
Inherent Difficulties of Non-Bayesian Likelihood-based Inference, as Revealed by an
Journal o/Cerebral Blood Flow and Metabolism 11:A26-A30 1991 The International Society of Cerebral Blood Flow and Metabolism
Ethics and Statistics Andrew Gelman,
december2011 183 2011 The Royal Statistical Society Tables as graphs
[Ethics and Statistics] Andrew Gelman