Gelman, Andrew

Bayesian Data Analysis

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · Chapman & Hall/CRC eBooks

Statistical Methods and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 27,276

Inference from iterative simulation using multiple sequences

Gelman, A., & Rubin, D. B. (1992) · Statistical Science

Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian Inference

Cited by 16,627

Data Analysis Using Regression and Multilevel/Hierarchical Models

Gelman, A., & Hill, J. (2006) · Cambridge University Press eBooks

Statistical Methods and Bayesian InferenceAdvanced Causal Inference Techniques

Cited by 13,597

Stan : A Probabilistic Programming Language

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software

Markov Chains and Monte Carlo MethodsData Analysis with R

Cited by 7,754

General methods for monitoring convergence of iterative simulations

Brooks, S. P., & Gelman, A. (1998) · Journal of Computational and Graphical Statistics

Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian Inference

Cited by 6,045

Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

Gelman, A. (2006) · Bayesian Analysis

Statistical Methods and Bayesian InferenceStatistical Distribution Estimation and Applications

Cited by 4,050

Understanding predictive information criteria for Bayesian models

Gelman, A., Hwang, J., & Vehtari, A. (2014) · Statistics and Computing

Statistical Methods and InferenceStatistical Methods and Bayesian Inference

Cited by 2,104

Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)

Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021) · Bayesian Analysis

Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian Inference

Cited by 1,810

Weak convergence and optimal scaling of random walk Metropolis algorithms

Roberts, G. O., Gelman, A., & Gilks, W. R. (1997) · The Annals of Applied Probability

Markov Chains and Monte Carlo MethodsTheoretical and Computational Physics

Cited by 1,693

The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant

Gelman, A., & Stern, H. (2006) · The American Statistician

Statistics Education and MethodologiesStatistical Methods and Bayesian Inference

Cited by 1,407

Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors

Gelman, A., & Carlin, J. (2014) · Perspectives on Psychological Science

Meta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

Cited by 1,343

Why We (Usually) Don't Have to Worry About Multiple Comparisons

Gelman, A., Hill, J., & Yajima, M. (2012) · Journal of Research on Educational Effectiveness

Statistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

Cited by 1,274

Visualization in Bayesian Workflow

Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019) · Journal of the Royal Statistical Society Series A: Statistics in Society

Statistical Methods and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 1,122

The Statistical Crisis in Science

Gelman, A., & Loken, E. (2014) · American Scientist

Philosophy of Biology and Scientific ExplanationStatistics Education and Methodologies

Cited by 963

Partisans without Constraint: Political Polarization and Trends in American Public Opinion

Baldassarri, D., & Gelman, A. (2008) · American Journal of Sociology

Electoral Systems and Political ParticipationMedia Influence and Politics

Cited by 874

An Analysis of the New York City Police Department's “Stop-and-Frisk” Policy in the Context of Claims of Racial Bias

Gelman, A., Fagan, J., & Kiss, A. (2007) · Journal of the American Statistical Association

Policing Practices and PerceptionsCriminal Justice and Corrections Analysis

Cited by 873

Analysis of variance—why it is more important than ever

Gelman, A. (2005) · The Annals of Statistics

Statistical Methods and Bayesian InferenceStatistical Methods and Applications

Cited by 861

Philosophy and the practice of Bayesian statistics

Gelman, A., & Shalizi, C. R. (2013) · British Journal of Mathematical and Statistical Psychology

Statistical Methods and Bayesian InferenceBayesian Modeling and Causal Inference

Cited by 825

Measurement error and the replication crisis

Loken, E., & Gelman, A. (2017) · Science

Meta-analysis and systematic reviewsPsychometric Methodologies and Testing

Cited by 628

Regression and Other Stories

Gelman, A., Hill, J., & Vehtari, A. (2020) · Cambridge University Press eBooks

Statistical Methods and Bayesian InferenceStatistical Methods and Applications

Cited by 613

Struggles with Survey Weighting and Regression Modeling

Gelman, A. (2007) · Statistical Science

Survey Sampling and Estimation TechniquesSurvey Methodology and Nonresponse

Cited by 604

Automatic differentiation variational inference

Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 515

Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

Park, D. K., Gelman, A., & Bafumi, J. (2004) · Political Analysis

Survey Sampling and Estimation TechniquesElectoral Systems and Political Participation

Cited by 480

Type S error rates for classical and Bayesian single and multiple comparison procedures

Gelman, A., & Tuerlinckx, F. (2000) · Computational Statistics

Statistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

Cited by 380