Click for files (in pdf format).

• [2025] The piranha problem: Large effects swimming in a small pond. {\em Notices of the American Mathematical Society}. (Christopher Tosh, Philip Greengard, Ben Goodrich, Andrew Gelman, and Daniel Hsu)

• [2025] For how many iterations should we run Markov chain Monte Carlo? In {\em Handbook of Markov Chain Monte Carlo}, second edition. (Charles C. Margossian and Andrew Gelman)

• [2024] Why forecast an election that’s too close to call? {\em Nature} {\bf 634}, 1019.

• [2024] Grappling with uncertainty in forecasting the 2024 U.S. presidential election. {\em Harvard Data Science Review} {\bf 6} (4). (Andrew Gelman, Ben Goodrich, and Geonhee Han)

• [2024] Review of ``Noise: A Flaw in Human Judgment,'' by Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein. {\em Chance} {\bf 37} (3), 70--72. (Gaurav Sood and Andrew Gelman)

• [2024] How statistical challenges and misreadings of the literature combine to produce unreplicable science: An example from psychology. {\em Advances in Methods and Practices in Psychological Science}. (Andrew Gelman and Nicholas J. L. Brown)

• [2024] Statistics as a social activity: Attitudes toward amalgamating evidence. {\em Entropy} {\bf 26} (8), 652. (Andrew Gelman and Keith O’Rourke)

• [2024] Nested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chains. {\em Bayesian Analysis}. (Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov, Lionel Riou-Durand, Aki Vehtari, and Andrew Gelman)

• [2024] Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale. {\em Statistics in Medicine} {\bf 43}, 953--982. (Swen Kuh, Lauren Kennedy, Qixuan Chen, and Andrew Gelman)

• [2024] Hopes and limitations of reproducible statistics and machine learning. {\em Harvard Data Science Review} {\bf 6} (1). (Andrew Gelman)

• [2024] Pareto smoothed importance sampling. {\em Journal of Machine Learning Research} {\bf 25} (72). (Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry)

• [2024] A new look at p-values for randomized clinical trials. {\em NEJM Evidence} {\bf 3} (1). (Erik van Zwet, Andrew Gelman, Sander Greenland, Guido Imbens, Simon Schwab, and Steven N. Goodman)

• [2024] Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity. {\em Bayesian Analysis}. (Martin Modrák, Angie H. Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, and Aki Vehtari)

• [2024] Before data analysis: Additional recommendations for designing experiments to learn about the world. {\em Journal of Consumer Psychology} {\bf 34}, 190--191. (Andrew Gelman)

• [2024] In pursuit of campus-wide data literacy: A guide to developing a statistics course for students in non-quantitative fields. {\em Journal of Statistics and Data Science Education} {\bf 32}, 241--252. (Alexis Lerner and Andrew Gelman)

• [2024] Causal quartets: Different ways to attain the same average treatment effect. {\em American Statistician} {\bf 78}, 267--272. (Andrew Gelman, Jessica Hullman, and Lauren Kennedy)

• [2023] Past, present, and future of software for Bayesian inference. {\em Statistical Science} {\bf 39}, 46--61. (Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, and Aki Vehtari)

• [2023] Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. {\em PLoS Computational Biology} 19, e1011580. (Thomas Ward, Mitzi Morris, Andrew Gelman, Bob Carpenter, William Ferguson, Christopher Overton, and Martyn Fylesn)

• [2023] Generically partisan: Polarization in political communication. {\em Proceedings of the National Academy of Sciences} 120, e2309361120. (Gustavo Novoa, Margaret Echelbarger, Andrew Gelman, and Susan Gelman)
  Supplementary appendix.

• [2023] Challenges in adjusting a survey that overrepresents people interested in politics. {\em Harvard Data Science Review} {\bf 5} (3). (Andrew Gelman and Gustavo Novoa)

• [2023] What is a standard error? {\em Journal of Econometrics} {\bf 237}, 105516. (Andrew Gelman)

• [2023] Who wants school vouchers in America? A comprehensive study using multilevel regression and poststratification. {\em Social Sciences} {\bf 12} (8), 430. (Yu-Sung Su and Andrew Gelman)

• [2023] A chain as strong as its strongest link? Understanding the causes and consequences of biases arising from selective analysis and reporting of research results. {\em Journal of Research on Educational Effectiveness} {\bf 17}, 459--461. (Andrew Gelman)

• [2023] Toward a taxonomy of trust for probabilistic machine learning. {\em Science Advances} {\bf 9}, eabn3999. (Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, and Tian Zheng)

• [2023] Federated learning as variational inference: A scalable expectation propagation approach. {\em International Conference on Learning Representations (ICLR)}. (Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, and Eric P. Xing)

• [2023] I love this paper but it's barely been noticed. Part of a collaborative article, ``What are your most underappreciated works?'' {\em Econ Journal Watch} {\bf 20}, 466. (Andrew Gelman)

• [2023] From visualization to sensification. {\em Amstat News} 547, 18--19. (Andrew Gelman and S. Gwynn Sturdevant)

• [2023] Fast methods for posterior inference of two-group normal-normal models. {\em Bayesian Analysis} {\bf 18}, 889--907. (Philip Greengard, Jeremy Hoskins, Charles C. Margossian, Jonah Gabry, Andrew Gelman, and Aki Vehtari)

• [2023] ``Two truths and a lie'' as a class-participation activity. {\em American Statistician} {\bf 77}, 97--101. (Andrew Gelman)

• [2023] Inference from non-random samples using Bayesian machine learning. {\em Journal of Survey Statistics and Methodology} {\bf 11}, 433--455. (Yutao Liu, Andrew Gelman, and Qixuan Chen)

• [2023] The Great Society, Reagan's revolution, and generations of presidential voting. {\em American Journal of Political Science} {\bf 67}, 520--537. (Yair Ghitza, Andrew Gelman, and Jonathan Auerbach)

• [2022] Pathfinder: Parallel quasi-Newton variational inference. {\em Journal of Machine Learning Research} {\bf 23}, 306. (Lu Zhang, Bob Carpenter, Andrew Gelman, and Aki Vehtari)

• [2022] Prediction scoring of data-driven discoveries for reproducible research. {\em Statistics and Computing} {\bf 33}, 11. (Anna L. Smith, Tian Zheng, and Andrew Gelman)

• [2022] The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning. {\em AIES '22: Fifth AAAI/ACM Conference on AI, Ethics, and Society}, 335--348. (Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, and Arvind Narayanan)

• [2022] Selecting on statistical significance and practical significance is wrong. {\em Journal of Information Technology} {\bf 37}, 312--315. (Blakeley McShane and Andrew Gelman)

• [2022] How should scientific journals handle ``Big if true'' submissions? {\em Chance} {\bf 35} (2), 41--43. (Andrew Gelman)

• [2022] No reason to expect large and consistent effects of nudge interventions. {\em Proceedings of the National Academy of Sciences} {\bf 119}, e2200732119. (Barnabás Szászi, Anthony C. Higney, Aaron B. Charlton, Andrew Gelman, Ignazio Ziano, Balacs Aczel, Daniel G. Goldstein, David S. Yeager, and Elizabeth Tipton)

• [2022] The development of Bayesian statistics. {\em Journal of the Indian Institute of Science} {\bf 102}, 1131--1134. (Andrew Gelman)

• [2022] Criticism as asynchronous collaboration: An example from social science research. {\em Stat} {\bf 11}, e464. (Andrew Gelman)

• [2022] Beyond vaccination rates: A synthetic random proxy metric of total SARS-CoV-2 immunity seroprevalence in the community. {\em Epidemiology} {\bf 33}, 457--464. (Yajuan Si, Leonard Covello, Siquan Wang, Theodore Covello, and Andrew Gelman)

• [2022] Stacking for non-mixing Bayesian computations: The curse and blessing of multimodal posteriors. {\em Journal of Machine Learning Research} {\bf 23}, 79. (Yuling Yao, Aki Vehtari, and Andrew Gelman)

• [2022] Reconciling evaluations of the Millennium Villages Project. {\em Statistics and Public Policy} {\bf 9}, 1--7. (Andrew Gelman, Shira Mitchell, Jeffrey D. Sachs, and Sonia Sachs)

• [2022] A proposal for informative default priors scaled by the standard error of estimates. {\em American Statistician} {\bf 76}, 1--9. (Erik van Zwet and Andrew Gelman)

• [2022] Bayesian hierarchical stacking: Some models are (somewhere) useful. {\em Bayesian Analysis} {\bf 17}, 1043--1071. (Yuling Yao, Gregor Pirš, Aki Vehtari, and Andrew Gelman)

• [2022] A fast linear regression via SVD and marginalization. {\em Computational Statistics} {\bf 37}, 701--720. (Philip Greengard, Andrew Gelman, and Aki Vehtari)

• [2022] Mismatch between scientific theories and statistical models. {\em Behavioral and Brain Sciences} {\bf 45}, e15. (Andrew Gelman)

• [2021] Ethical requirements of a research assistant who is concerned about the behavior of a supervisor. {\em Chance} {\bf 34} (4), 21--22. (Andrew Gelman)

• [2021] Designing for interactive exploratory data analysis requires theories of graphical inference (with discussion). {\em Harvard Data Science Review} {\bf 3} (3). (Jessica Hullman and Andrew Gelman)
  Challenges in incorporating exploratory data analysis into statistical workflow (rejoinder to discussion). (Jessica Hullman and Andrew Gelman)

• [2021] Failure and success in political polling and election forecasting. {\em Statistics and Public Policy} {\bf 8}, 67--72. (Andrew Gelman)

• [2021] How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis. {\em Linguistics} {\bf 59}, 1311--1342. (Shravan Vasishth and Andrew Gelman)

• [2021] Accounting for uncertainty during a pandemic. {\em Patterns} {\bf 2}, 100310. (Jon Zelner, Julien Riou, Ruth Etzioni, and Andrew Gelman)

• [2021] Research on registered report research. {\em Nature Human Behaviour} {\bf 5}, 978--979. (Megan Higgs and Andrew Gelman)

• [2021] What are the most important statistical ideas of the past 50 years? {\em Journal of the American Statistical Association} {\bf 116}, 2087--2097. (Andrew Gelman and Aki Vehtari)

• [2021] A simple explanation for declining temperature sensitivity with warming. {\em Global Change Biology} {\bf 27}, 4947--4949. (E. M. Wolkovich, J. L. Auerbach, C. J. Chamberlain, D. M. Buonaiuto, A. K. Ettinger, I. Morales-Castilla, and A. Gelman)
  Supplementary appendix.

• [2021] Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden. {\em Epidemiology} {\bf 32}, 792--799. (Len Covello, Andrew Gelman, Yajuan Si, and Siquan Wang)

• [2021] Why did it take so many decades for the behavioral sciences to develop a sense of crisis around methodology and replication? {\em Journal of Methods and Measurement in the Social Sciences} {\bf 12}, 37--41. (Andrew Gelman and Simine Vazire)

• [2021] Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data. {\em Statistics in Medicine} {\bf 40}, 3403--3424. (Andrew Gelman and Matthijs Vákár)

• [2021] Social penumbras predict political attitudes. {\em Proceedings of the National Academy of Sciences} {\bf 118} (6), e2019375118. (Andrew Gelman and Yotam Margalit)

• [2021] Reflections on Lakatos's ``Proofs and Refutations.'' {\em American Mathematical Monthly} {\bf 128}, 191--192. (Andrew Gelman)

• [2021] Holes in Bayesian statistics. {\em Journal of Physics G: Nuclear and Particle Physics} {\bf 48}, 014002. (Andrew Gelman and Yuling Yao)

• [2021] Reflections on Breiman's Two Cultures of Statistical Modeling. {\em Observational Studies} {\bf 7}, 95--98. (Andrew Gelman)

• [2021] Bayesian statistics and modelling. {\em Nature Reviews Methods Primers} {\bf 1}, 1. (Rens van de Schoot, Sarah Depaoli, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, Andrew Gelman, Duco Veen, Joukje Willemsen, and Christopher Yau)

• [2021] Community prevalence of SARS-CoV-2 in England: Results from the ONS Coronavirus Infection Survey Pilot. {\em Lancet Public Health} {\bf 6}, E30--E38. (Koen B. Pouwels, Thomas House, Emma Pritchard, Julie V. Robotham, Paul J. Birrell, Andrew Gelman, Karina-Doris Vihta, Nikola Bowers, Ian Boreham, Heledd Thomas, James Lewis, Iain Bell, John I. Bell, John N. Newton, Jeremy Farrar, Ian Diamond, Pete Benton, Ann Sarah Walker, and the COVID-19 Infection Survey Team)

• [2021] Improving multilevel regression and poststratification with structured priors. {\em Bayesian Analysis} {\bf 16}, 719--744. (Yuxiang Gao, Lauren Kennedy, Daniel Simpson, and Andrew Gelman)

• [2021] Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample. {\em Psychological Methods} {\bf 26}, 547--558. (Lauren Kennedy and Andrew Gelman)

• [2021] Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. {\em Bayesian Analysis} {\bf 16}, 667--718. (Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner)

• [2020] Information, incentives, and goals in election forecasts. {\em Judgment and Decision Making} {\bf 15}, 863--880. (Andrew Gelman, Jessica Hullman, Christopher Wlezien, and George Elliott Morris)

• [2020] An updated dynamic Bayesian forecasting model for the 2020 election. {\em Harvard Data Science Review} {\bf 2} (4). (Merlin Heidemanns, Andrew Gelman, and Elliott Morris)

• [2020] Bayesian hierarchical weighting adjustment and survey inference. {\em Survey Methodology} {\bf 46}, 181--214. (Yajuan Si, Rob Trangucci, Jonah Gabry, and Andrew Gelman)

• [2020] Bayesian analysis of tests with unknown specificity and sensitivity. {\em Journal of the Royal Statistical Society C, Applied Statistics} {\bf 69}, 1269--1284. (Andrew Gelman and Bob Carpenter)

• [2020] Fallout of lead over Paris from the 2019 Notre-Dame cathedral fire. {\em GeoHealth} {\bf 4} (8). (Alexander van Geen, Yuling Yao, Tyler Ellis, and Andrew Gelman)

• [2020] Assessing evidence vs.\ truth in the coronavirus pandemic. {\em Chance} {\bf 33} (3), 58--60. (Andrew Gelman)

• [2020] Using Bayesian analysis to account for uncertainty and adjust for bias in coronavirus sampling. {\em International Society for Bayesian Analysis Bulletin} {\bf 27} (2), 11--12. (Andrew Gelman and Bob Carpenter)

• [2020] Data visualization as narrative. {\em Frieze} {\bf 213}. (Andrew Gelman and Helen DeWitt)

• [2020] Lessons learned and remaining challenges for online seminars and conferences. {\em Amstat News}, 1 July. (Lauren Kennedy, Guillaume Basse, Andrew Gelman, Guido Imbens​, Yajuan Si, Dominik Rothenhausler​, and Jann Spiess​)

• [2020] Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data. {\em Journal of Machine Learning Research} {\bf 21}, 17. (Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylanki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, and Christian P. Robert)

• [2020] Laplace's theories of cognitive illusions, heuristics, and biases (with discussion). {\em Statistical Science} {\bf 35}, 159--170. (Joshua B. Miller and Andrew Gelman)
  Rejoinder to discussion. {\em Statistical Science} {\bf 35}, 175--177. (Joshua B. Miller and Andrew Gelman)

• [2020] Statistics as squid ink: How prominent researchers can get away with misrepresenting data. {\em Chance} {\bf 33} (2), 25--27. (Andrew Gelman and Alexey Guzey)

• [2020] Voter registration databases and MRP: Toward the use of large scale databases in public opinion research. {\em Political Analysis} {\bf 28}, 507--531. (Yair Ghitza and Andrew Gelman)

• [2020] A consensus-based transparency checklist. {\em Nature Human Behaviour} {\bf 4}, 561--563. (Balazs Aczel, Barnabas Szaszi, Alexandra Sarafoglou, Zoltan Kekecs, Šimon Kucharský, Daniel Benjamin, Christopher Chambers, Agneta Fisher, Andrew Gelman, et al.)

• [2020] Type M error might explain Weisburd's Paradox. {\em Journal of Quantitative Criminology} {\bf 36}, 295--304. (Andrew Gelman, Torbjørn Skardhamar, and Mikko Aaltonen)

• [2019] Are confidence intervals better termed ``uncertainty intervals''? {\em British Medical Journal} {\bf 366}, l5381. (Andrew Gelman and Sander Greenland)

• [2019] When we make recommendations for scientific practice, we are (at best) acting as social scientists. {\em European Journal of Clinical Investigation} {\bf 49} (10), e13165. (Andrew Gelman)

• [2019] Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in Stan. {\em Spatial and Spatio-temporal Epidemiology} {\bf 31}, 100301. (Mitzi Morris, Katherine Wheeler-Martin, Daniel Simpson, Stephen Mooney, Andrew Gelman, and Charles DiMaggio)

• [2019] The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling. {\em Computational Brain and Behavior} {\bf 2}, 210--217. (Lauren Kennedy, Daniel Simpson, and Andrew Gelman)

• [2019] Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims. {\em Obesity Reviews} {\bf 20}, 1523--1541. (Andrew Brown, Douglas Altman, Tom Baranowski, J. Martin Bland, John Dawson, Nikhil Dhurandhar, Shima Dowla, Kevin Fontaine, Andrew Gelman, Steven Heymsfield, Wasantha Jayawardene, Scott Keith, Theodore Kyle, Eric Loken, J. Michael Oakes, June Stevens, Diana Thomas, and David Allison)

• [2019] The implementation of randomization requires corrected analyses. Comment on ``Comprehensive nutritional and dietary intervention for autism spectrum disorder---A randomized, controlled 12-month trial, Nutrients 2018, 10, 369. {\em Nutrients} {\bf 11}, 1126. (Colby J. Vorland, Andrew W. Brown, Stephanie L. Dickinson, Andrew Gelman, and David B. Allison)

• [2019] Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina (ORBITA) and coronary stents: A case study in the analysis and reporting of clinical trials. {\em American Heart Journal} {\bf 214}, 54--59. (Andrew Gelman, John Carlin, and Brahmajee Nallamothu)

• [2019] The principles of uncertainty. Review of ``Do Dice Play God,'' by Ian Stewart. {\em Nature} {\bf 569}, 628--629. (Andrew Gelman)

• [2019] Post-hoc power using observed estimate of effect size is too noisy to be useful. {\em Annals of Surgery} {\bf 270}, e64. (Andrew Gelman)

• [2019] Multiple perspectives on inference for two simple statistical scenarios. {\em American Statistician} {\bf 73} (S1), 328--339. (Noah N. N. van Dongen, Johnny B. van Doorn, Quentin F. Gronau, Don van Ravenzwaaij, Rink Hoekstra, Matthias N. Haucke, Daniel Lakens, Christian Hennig, Richard D. Morey, Saskia Homer, Andrew Gelman, Jan Sprenger, and Eric-Jan Wagenmakers)

• [2019] Abandon statistical significance. {\em American Statistician} {\bf 73} (S1), 235--245. (Blakeley B. McShane, David Gal, Andrew Gelman, Christian Robert, and Jennifer L. Tackett)

• [2019] Large scale replication projects in contemporary psychological research. {\em American Statistician} {\bf 73} (S1), 99--105. (Blakeley B. McShane, Jennifer L. Tackett, Ulf Bockenholt, and Andrew Gelman)

• [2019] Don't calculate post-hoc power using observed estimate of effect size. {\em Annals of Surgery} {\bf 269}, e9--e10. (Andrew Gelman)

• [2019] Limitations of ``Limitations of Bayesian leave-one-out cross-validation for model selection.'' {\em Computational Brain and Behavior} {\bf 2}, 22--27. (Aki Vehtari, Daniel P. Simpson, Yuling Yao, and Andrew Gelman)

• [2019] Why high-order polynomials should not be used in regression discontinuity designs. {\em Journal of Business and Economic Statistics} {\bf 37}, 447--456. (Andrew Gelman and Guido Imbens)

• [2019] Visualization in Bayesian workflow (with discussion and rejoinder). {\em Journal of the Royal Statistical Society A} {\bf 182}, 389--402. (Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman)

• [2018] R-squared for Bayesian regression models. {\em American Statistician} {\bf 73}, 307--309. (Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari)

• [2018] The statistical significance filter leads to overconfident expectations of replicability. {\em Journal of Memory and Language} {\bf 103}, 151--175. (Shravan Vasishth, Daniela Mertzen, Lena A. Jäger, and Andrew Gelman)

• [2018] Do researchers anchor their beliefs on the outcome of an initial study? Testing the time-reversal heuristic. {\em Experimental Psychology} {\bf 65}, 158--169. (Anja Ernst, Rink Hoekstra, Eric-Jan Wagenmakers, Andrew Gelman, and Don van Ravenzwaaij)

• [2018] Ethics in statistical practice and communication: Five recommendations. {\em Significance} {\bf 15} (5), 40--43. (Andrew Gelman)

• [2018] Bayesian inference under cluster sampling with probability proportional to size. {\em Statistics in Medicine} {\bf 37}, 3849--3868. (Susanna Makela, Yajuan Si, and Andrew Gelman)

• [2018] Yes, but did it work?: Evaluating variational inference. {\em Proceedings of Machine Learning Research} {\bf 80}, 5581--5590. (Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman)

• [2018] Gaydar and the fallacy of decontextualized measurement. {\em Sociological Science} {\bf 5}, 270--280. (Andrew Gelman, Greggor Matson, and Daniel Simpson)

• [2018] Global shifts in the phenological synchrony of species interactions over recent decades. {\em Proceedings of the National Academy of Sciences} {\bf 115} (20), 5211--5216. (Heather M. Kharouba, Johan Ehrlén, Andrew Gelman, Kjell Bolmgren, Jenica M. Allen, Steve E. Travers, and Elizabeth M. Wolkovich)

• [2018] The Millennium Villages Project: A retrospective, observational, endline evaluation. {\em Lancet Global Health} {\bf 6} (5), e500--e513. (Shira Mitchell, Andrew Gelman, Rebecca Ross, Joyce Chen, Sehrish Bari, Uyen Kim Huynh, Matthew W. Harris, Sonia Ehrlich Sachs, Elizabeth A. Stuart, Avi Feller, Susanna Makela, Alan M. Zaslavsky, Lucy McClellan, Seth Ohemeng-Dapaah, Patricia Namakula, Cheryl A. Palm, and Jeffrey D. Sachs)
  Supplementary appendix.

• [2018] Disentangling bias and variance in election polls. {\em Journal of the American Statistical Association} {\bf 113}, 607--614. (Houshmand Shirani-Mehr, David Rothschild, Sharad Goel, and Andrew Gelman)

• [2018] Don't characterize replications as successes or failures. Discussion of ``Making replication mainstream,'' by Rolf A. Zwaan et al. {\em Behavioral and Brain Sciences} {\bf 41}, e128. (Andrew Gelman)

• [2018] Using stacking to average Bayesian predictive distributions (with discussion). {\em Bayesian Analysis} {\bf 13}, 917--1003. (Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman)

• [2018] Benefits and limitations of randomized controlled trials. Discussion of ``Understanding and misunderstanding randomized controlled trials,'' by Angus Deaton and Nancy Cartwright. {\em Social Science \& Medicine} {\bf 210}, 48--49. (Andrew Gelman)

• [2018] The failure of null hypothesis significance testing when studying incremental changes, and what to do about it. {\em Personality and Social Psychology Bulletin} {\bf 44}, 16--23. (Andrew Gelman)

• [2018] Bayesian aggregation of average data: An application in drug development. {\em Annals of Applied Statistics} {\bf 12}, 1583--1604. (Sebastian Weber, Andrew Gelman, Daniel Lee, Michael Betancourt, Aki Vehtari, and Amy Racine-Poon)

• [2018] How to think scientifically about scientists' proposals for fixing science. {\em Socius} {\bf 4}, 1--2. (Andrew Gelman)

• [2018] Learning from and responding to statistical criticism. {\em Observational Studies} {\bf 4}, 32--33. (Andrew Gelman)

• [2018] Donald Rubin. In {\em Encyclopedia of Social Research Methods}, ed.\ Paul Atkinson, Sara Delamont, Melissa Hardy, and Malcolm Williams. Thousand Oaks, Calif.: Sage Publications. (Andrew Gelman)

• [2017] The prior can often only be understood in the context of the likelihood. {\em Entropy} {\bf 19}, 555. (Andrew Gelman, Daniel Simpson, and Michael Betancourt)

• [2017] Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. {\em Statistics and Computing} {\bf 27}, 1413--1432. (Aki Vehtari, Andrew Gelman, and Jonah Gabry)

• [2017] 19 things we learned from the 2016 election (with discussion). {\em Statistics and Public Policy} {\bf 4} (1), 1--10. (Andrew Gelman and Julia Azari)
  How special was 2016? (rejoinder to discussion). (Julia Azari and Andrew Gelman)

• [2017] Exploring the relationships between USMLE performance and disciplinary action in practice: A validity study of score inferences from a licensure examination. {\em Academic Medicine} {\bf 92}, 1780--1785. (Monica M. Cuddy, Aaron Young, Andrew Gelman, David B. Swanson, David A. Johnson, Gerard F. Dillon, and Brian E. Clauser)

• [2017] Some natural solutions to the p-value communication problem---and why they won't work. {\em Journal of the American Statistical Association} {\bf 112}, 899--901. (Andrew Gelman and John B. Carlin)

• [2017] Beyond subjective and objective in statistics (with discussion and rejoinder). {\em Journal of the Royal Statistical Society A} {\bf 180}, 967--1033. (Andrew Gelman and Christian Hennig)

• [2017] Measurement error and the replication crisis. {\em Science} {\bf 355}, 584--585. (Eric Loken and Andrew Gelman)

• [2017] Honesty and transparency are not enough. {\em Chance} {\bf 30} (1), 37--39. (Andrew Gelman)

• [2017] Stan: A probabilistic programming language. {\em Journal of Statistical Software} {\bf 76} (1). (Bob Carpenter, Andrew Gelman, Matt Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell)

• [2017] Consensus Monte Carlo using expectation propagation. {\em Brazilian Journal of Probability and Statistics} {\bf 31}, 692--696. (Andrew Gelman and Aki Vehtari)

• [2017] The 2008 election: A preregistered replication analysis. {\em Statistics and Public Policy} {\bf 4} (1), 1--8. (Rayleigh Lei, Andrew Gelman, and Yair Ghitza)
  Online appendix.

• [2017] The statistical crisis in science: How is it relevant to clinical neuropsychology? {\em Clinical Neuropsychologist} {\bf 31}, 1000--1014. (Andrew Gelman and Hilde Geurts)

• [2017] Automatic differentiation variational inference. {\em Journal of Machine Learning Research} {\bf 18}, 14. (Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei)

• [2017] Learning about networks using sampling. {\em Journal of Survey Statistics and Methodology} {\bf 5}, 22--28. (Andrew Gelman)

• [2017] Fitting Bayesian item response models in Stata and Stan. {\em Stata Journal} {\bf 17}, 343--357. (Robert Grant, Daniel Furr, Bob Carpenter, and Andrew Gelman)

• [2016] Questionable association between front boarding and air rage. {\em Proceedings of the National Academy of Sciences} {\bf 113}, E7348. (Marcus Crede, Andrew Gelman, and Carol Nickerson)

• [2016] Age-aggregation bias in mortality trends. {\em Proceedings of the National Academy of Sciences} {\bf 113}, E816--E817. (Andrew Gelman and Jonathan Auerbach)

• [2016] A Bayesian bird's eye view of `Replications of important results in social psychology.' {\em Royal Society Open Science} {\bf 4}, 160426. (Maarten Marsman, Felix Schoonbrodt, Richard Morey, Yuling Yao, Andrew Gelman, and Eric-Jan Wagenmakers)

• [2016] Commentary on ``Crisis in science? Or Crisis in statistics! Mixed messages in statistics with impact on science,'' by Donald A. S. Fraser and Nancy M. Reid. {\em Journal of Statistical Research} {\bf 48--50}, 11--12. (Andrew Gelman)

• [2016] High-frequency polling with non-representative data. In {\em Political Communication in Real Time: Theoretical and Applied Research Approaches}, 89--105. (Andrew Gelman, Sharad Goel, David Rothschild, and Wei Wang)

• [2016] Increasing transparency through a multiverse analysis. {\em Perspectives on Psychological Science} {\bf 11}, 702--712. (Sara Steegen, Francis Tuerlinckx, Andrew Gelman, and Wolf Vanpaemel)
  Supplemental materials.

• [2016] The problems with p-values are not just with p-values. {\em American Statistician} {\bf 70}. (Andrew Gelman)

• [2016] Will public opinion about inequality be packaged into neatly partisan positions? {\em Pathways}, Winter, 27--32. (Andrew Gelman and Leslie McCall)

• [2016] The mythical swing voter. {\em Quarterly Journal of Political Science} {\bf 11}, 103--130. (Andrew Gelman, Sharad Goel, Douglas Rivers, and David Rothschild)

• [2016] Graphical visualization of polling results. In {\em Oxford Handbook on Polling and Polling Methods}, ed.\ Lonna Atkeson and Michael Alvarez. (Susanna Makela, Yajuan Si, and Andrew Gelman)

• [2015] How better educated whites are driving political polarization. In {\em Political Polarization in American Politics}, ed.\ Daniel J. Hopkins and John Sides, 91--94. (Andrew Gelman)

• [2015] Automatic variational inference in Stan. In {\em Advances in Neural Information Processing Systems} ed.\ C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, 568--576. (Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, and David Blei)

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• [2014] Statistical graphics for survey weights. {\em Revista Colombiana de Estadistica} {\bf 37}, 285--295. (Susanna Makela, Yajuan Si, and Andrew Gelman)

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• [2014] How do we choose our default methods? For the Committee of Presidents of Statistical Societies (COPSS) 50th anniversary volume. (Andrew Gelman)

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• [2014] When do stories work? Evidence and illustration in the social sciences. {\em Sociological Methods and Research} {\bf 43}, 547--570. (Andrew Gelman and Thomas Basboll)

• [2014] Multiple imputation for continuous and categorical data: Comparing joint and conditional approaches. {\em Political Analysis} {\bf 22}, 497--519. (Jonathan Kropko, Ben Goodrich, Andrew Gelman, and Jennifer Hill)

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• [2014] The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa? {\em Statistics and Public Policy} {\bf 1}, 1--5. (Andrew Gelman)

• [2014] On the stationary distribution of iterative imputations. {\em Biometrika} {\bf 1}, 155--173. (Jingchen Liu, Andrew Gelman, Jennifer Hill, Yu-Sung Su, and Jonathan Kropko)

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• [2014] Understanding predictive information criteria for Bayesian models. {\em Statistics and Computing} {\bf 24}, 997--1016. (Andrew Gelman, Jessica Hwang, and Aki Vehtari)

• [2014] Difficulties in making inferences about scientific truth from distributions of published p-values. {\em Biostatistics} {\bf 1}, 18--23. (Andrew Gelman and Keith O'Rourke)

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• [2014] Convincing evidence. In {\em Roles, Trust, and Reputation in Social Media Knowledge Markets}, ed.\ Sorin Matei and Elisa Bertino. (Andrew Gelman and Keith O'Rourke)

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• [2013] In praise of the referee. {\em International Society for Bayesian Analysis Bulletin} {\bf 20} (1), 13--19. (Nicolas Chopin, Andrew Gelman, Kerrie Mengersen, and Christian Robert)

• [2013] It's too hard to publish criticisms and obtain data for replication. {\em Chance} {\bf 26} (3), 49--52. (Andrew Gelman)

• [2013] To throw away data: Plagiarism as a statistical crime. {\em American Scientist} {\bf 101}, 168--171. (Andrew Gelman and Thomas Basboll)

• [2013] Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin. {\em Statistics \& Risk Modeling} {\bf 30}, 1001--1016. (Andrew Gelman, Christian Robert, and Judith Rousseau).

• [2013] Interrogating P-values. {\em Journal of Mathematical Psychology} {\bf 57}, 188--189. (Andrew Gelman)

• [2013] Nonparametric models can be checked. {\em Bayesian Analysis} {\bf 8}, 332--333. (Andrew Gelman)

• [2013] They'd rather be rigorous than right. {\em Chance} {\bf 26} (2), 45--49. (Andrew Gelman)

• [2013] Infovis and statistical graphics: Different goals, different looks (with discussion). {\em Journal of Computational and Graphical Statistics} {\bf 22}, 2--49. (Andrew Gelman and Antony Unwin)
  Tradeoffs in information graphics (rejoinder to discussion). (Andrew Gelman and Antony Unwin)

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• [2013] The war on data. {\em Chance} {\bf 26} (1), 57--60. (Andrew Gelman and Mark Palko)

• [2013] Charles Murray's {\em Coming Apart} and the measurement of social and political divisions. {\em Statistics, Politics and Policy} {\bf 4}, 70--81. (Andrew Gelman)

• [2013] A practical guide to measuring social structure using indirectly observed network data. {\em Journal of Statistical Theory and Practice} {\bf 7}, 120--132. (Tyler McCormick, Amal Moussa, Johannes Ruf, Thomas DiPrete, Andrew Gelman, Julien Teitler, and Tian Zheng)

• [2013] A nondegenerate estimator for hierarchical variance parameters via penalized likelihood estimation. {\em Psychometrika} {\bf 78}, 685--709. (Yeojin Chung, Sophia Rabe-Hesketh, Andrew Gelman, Jingchen Liu, and Vincent Dorie)

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  The anti-Bayesian moment and its passing (rejoinder to discussion). (Andrew Gelman and Christian Robert)

• [2013] Preregistration of studies and mock reports. {\em Political Analysis} {\bf 21}, 40--41. (Andrew Gelman)

• [2013] Philosophy and the practice of Bayesian statistics (with discussion). {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 8--38. (Andrew Gelman and Cosma Shalizi)
  Rejoinder to discussion. {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 76--80. (Andrew Gelman and Cosma Shalizi)

• [2013] Rates and correlates of HIV and STI infection among homeless women. {\em AIDS and Behavior} {\bf 17}, 856-864. (Carol L. M. Caton, Nabila El-Bassel, Andrew Gelman, Susan Barrow, Daniel Herman, Eustace Hsu, Ana Z. Tochterman, Karen Johnson, and Alan Felix)

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• [2012] Red state / blue state divisions in the 2012 presidential election. {\em The Forum} {\bf 10}, 127--141. (Avi Feller, Andrew Gelman, and Boris Shor)

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• [2012] Estimating partisan bias of the electoral college under proposed changes in elector apportionment. {\em Statistics, Politics and Policy} {\bf 3}, 1--13. (Andrew C. Thomas, Andrew Gelman, Gary King, and Jonathan Katz)

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• [2012] Understanding persuasion and activation in presidential campaigns: The random walk and mean-reversion models. {\em Presidential Studies Quarterly} {\bf 42}, 843--866. (Noah Kaplan, David K. Park, and Andrew Gelman)

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• [2012] Statistics for sellers of cigarettes. {\em Chance} {\bf 25} (3), 43--46. (Andrew Gelman)

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• [2011] Inference from simulations and monitoring convergence. In {\em Handbook of Markov Chain Monte Carlo}, ed.\ S. Brooks, A. Gelman, G. Jones, and X. L. Meng. CRC Press. (Andrew Gelman and Kenneth Shirley)

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• [2008] Partisans without constraint: political polarization and trends in American public opinion. {\em American Journal of Sociology} {\bf 114}, 408--446. (Delia Baldassarri and Andrew Gelman)

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• [2008] Diagnostics for multivariate imputations. {\em Journal of the Royal Statistical Society C, Applied Statistics} {\bf 57}, 273--291. (Kobi Abayomi, Andrew Gelman, and Marc Levy)

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• [2007] Letter to the editors regarding some papers of Dr. Satoshi Kanazawa. {\em Journal of Theoretical Biology} {\bf 245}, 597--599. (Andrew Gelman)

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• [2007] Evaluation of multilevel decision trees. {\em Journal of Statistical Planning and Inference} {\bf 137}, 1151--1160. (Erwann Rogard, Andrew Gelman, and Hao Lu)

• [2007] Review of ``Regression Analysis: A Constructive Critique,'' by Richard Berk. {\em Criminal Justice Review} {\bf 32}, 301--302. (Andrew Gelman)

• [2006] Tools for Bayesian data analysis in R. {\em Statistical Computing and Graphics} {\bf 17} (2), 9--13. (Jouni Kerman and Andrew Gelman)

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  Figures for this paper

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• [2006] Multilevel (hierarchical) modeling: What it can and cannot do. {\em Technometrics} {\bf 48}, 241--251. (Andrew Gelman)

• [2006] Output assessment for Monte Carlo simulations via the score statistic. {\em Journal of Computational and Graphical Statistics} {\bf 15}, 178--206. (Yanan Fan, Stephen Brooks, and Andrew Gelman)

• [2005] State-level opinions from national surveys: Poststratification using multilevel logistic regression. In {\em Public Opinion in State Politics}, ed.\ J. E. Cohen. Stanford University Press. (David K. Park, Andrew Gelman, and Joseph Bafumi)

• [2005] Two-stage regression and multilevel modeling: a commentary. {\em Political Analysis} {\bf 13}, 459--461. (Andrew Gelman)

• [2005] Anova as a tool for structuring and understanding hierarchical models. {\em Chance} {\bf 18} (3), 33. (Andrew Gelman)

• [2005] An experimental study of storable votes. {\em Games and Economic Behavior} {\bf 57}, 123--154. (Alessandra Casella, Andrew Gelman, and Thomas R. Palfrey)

• [2005] R2WinBUGS: A package for running WinBUGS from R. {\em Journal of Statistical Software} {\bf 12} (3). (Sibylle Sturtz, Uwe Ligges, and Andrew Gelman)

• [2005] A course on teaching statistics at the university level. {\em American Statistician} {\bf 59}, 4--7. (Andrew Gelman)

• [2005] Probabilistic feature analysis of facial perception of emotions. {\em Journal of the Royal Statistical Society C, Applied Statistics} {\bf 54}, 781--793. (Michel Meulders, Paul De Boeck, Ivan Van Mechelen, and Andrew Gelman)

• [2005] Analysis of variance: Why it is more important than ever (with discussion). {\em Annals of Statistics} {\bf 33}, 1--53. (Andrew Gelman)

• [2005] Practical issues in implementing and understanding Bayesian ideal point estimation. {\em Political Analysis} {\bf 13}, 171--187. (Joseph Bafumi, Andrew Gelman, David K. Park, and Noah Kaplan)

• [2005] Should you measure the radon concentration in your home? In {\em Statistics: A Guide to the Unknown}, fourth edition. (Phillip N. Price and Andrew Gelman)

• [2005] Multiple imputation for model checking: Completed-data plots with missing and latent data. {\em Biometrics} {\bf 61}, 74--85. (Andrew Gelman, Iven Van Mechelen, Geert Verbecke, Daniel F. Heitjan, and Michel Meulders)

• [2004] Reliability of a commercial kit to test groundwater for arsenic in Bangladesh. {\em Environmental Science and Technology} {\bf 39}, 299--303. (A. van Geen, Z. Cheng, A. A. Seddique, M. A. Hoque, A. Gelman, J. H. Graziano, H. Ahsan, F. Parvez, and K. M. Ahmed)

• [2004] Direct data manipulation for local decision analysis, as applied to the problem of arsenic in drinking water from tube wells in Bangladesh. {\em Risk Analysis} {\bf 24}, 1597--1612. (Andrew Gelman, Matilde Trevisani, Hao Lu, and Alexander van Geen)

• [2004] Bayesian multilevel estimation with poststratification: State-level estimates from national polls. {\em Political Analysis} {\bf 12}, 375--385. (David K. Park, Andrew Gelman, and Joseph Bafumi)

• [2004] Treatment effects in before-after data. In {\em Applied Bayesian Modeling and Causal Inference from an Incomplete Data Perspective}, ed.\ A. Gelman and X. L. Meng, chapter 18. London: Wiley. (Andrew Gelman)

• [2004] Using image and curve registration for measuring the goodness of fit of spatial and temporal predictions. {\em Biometrics} {\bf 60}, 954--964. (Cavan Reilly, Phillip Price, Andrew Gelman, and Scott A. Sandgathe)

• [2004] A broken system: The persistent pattern of reversals of death sentences in the United States. {\em Journal of Empirical Legal Studies} {\bf 1}, 209--261. (Andrew Gelman, James Liebman, Valerie West, and Alex Kiss)

• [2004] 55,000 residents desperately need your help! {\em Chance} {\bf 17} (2), 28--30. (Andrew Gelman)

• [2004] Exploratory data analysis for complex models (with discussion). {\em Journal of Computational and Graphical Statistics} {\bf 13}, 755--779. (Andrew Gelman)
  Discussion of this paper by Andreas Buja. {\em Journal of Computational and Graphical Statistics} {\bf 13}, 780--784. (Andreas Buja)
  Rejoinder to discussion. {\em Journal of Computational and Graphical Statistics} {\bf 13}, 785--787. (Andrew Gelman)

• [2004] Bayesian analysis of serial dilution assays. {\em Biometrics} {\bf 60}, 407--417. (Andrew Gelman, Ginger Chew, and Michael Shnaidman)

• [2004] Standard voting power indexes don't work: An empirical analysis. {\em British Journal of Political Science} {\bf 34}, 657--674. (Andrew Gelman, Jonathan N. Katz, and Joseph Bafumi)

• [2004] Extension of the isobolographic approach to interactions studies between more than two drugs: Illustration with the convulsant interaction between pefloxacin, norfloxacin, and theophylline in rats. {\em Journal of Pharmaceutical Sciences} {\bf 93}, 553--562. (Celine Brochot, Sandrine Marchand, William Conet, Andrew Gelman, and Frederic Y. Bois)

• [2004] Parameterization and Bayesian modeling. {\em Journal of the American Statistical Association} {\bf 99}, 537--545. (Andrew Gelman)

• [2004] Empirically evaluating the electoral college. In {\em Rethinking the Vote: The Politics and Prospects of American Election Reform}, ed.\ A. N. Crigler, M. R. Just, and E. J. McCaffery, 75--88. Oxford University Press. (Andrew Gelman, Jonathan N. Katz, and Gary King)

• [2003] Forming voting blocs and coalitions as a prisoner's dilemma: a possible theoretical explanation for political instability. {\em Contributions to Economic Analysis and Policy} {\bf 2} (1), article 13. (Andrew Gelman)

• [2003] A Bayesian formulation of exploratory data analysis and goodness-of-fit testing. {\em International Statistical Review} {\bf 71}, 369--382. (Andrew Gelman)

• [2003] A method for estimating design-based sampling variances for surveys with weighting, poststratification, and raking. {\em Journal of Official Statistics} {\bf 19}, 133--151. (Hao Lu and Andrew Gelman)

• [2003] Spatial variability of arsenic in 6000 tube wells in a 25 km$^2$ area of Bangladesh. {\em Water Resources Research} {\bf 39} (5), 1140. (Van Geen, A., Zheng, Y., Versteeg, R., Stute, M., Horneman, A., Dhar, R., Steckler, M., Gelman, A., Small, C., Ahsan, H., Graziano, J. H., Hussein, I., and Ahmed, K. M.)

• [2003] A Bayesian approach to the selection and testing of latent class models. {\em Statistica Sinica} {\bf 13}, 423--442. (Johannes Berkhof, Iven Van Mechelen, and Andrew Gelman)

• [2003] Assessing the implementation and effects of a trauma-focused intervention for youths in residential treatment. {\em Psychiatric Quarterly} {\bf 74}, 137--154. (Jeanne C. Rivard, Sandra L. Bloom, Robert Abramovitz, Lina E. Pasquale, Mariama Duncan, David McCorkle, and Andrew Gelman)

• [2003] Regression models for decision making: A cost-benefit analysis of incentives in telephone surveys. {\em Journal of Business and Economic Statistics} {\bf 21}, 213--225. (Andrew Gelman, Matt Stevens, and Valerie Chan)

• [2002] The mathematics and statistics of voting power. {\em Statistical Science} {\bf 17}, 420--435. (Andrew Gelman, Jonathan N. Katz, and Francis Tuerlinckx)

• [2002] Promotion of well-switching to mitigate the arsenic crisis in Bangladesh. {\em Bulletin of the World Health Organization} {\bf 80}, 732--737. (A. van Geen, H. Ahsan, A. Horneman, R. K. Dhar, Y. Zheng, A. Z. M. I. Hussain, K. M. Ahmed, A. Gelman, M. Stute, H. J. Simpson, S. Wallace, C. Small, M. F. Parvez, V. Slavkovich, N. J. LoIacono, M. Becker, Z. Cheng, H. Momotaj, M. Shahnewaz, A. A. Seddique, and J. Graziano)

• [2002] Mechanistic understanding of models for educational assessments. Discussion of ``On the structure of educational assessments,'' by R. Mislevy et al. {\em Measurement: Interdisciplinary Research and Perspectives} {\bf 1}, 73--76. (Andrew Gelman)

• [2002] Let's practice what we preach: Turning tables into graphs. {\em American Statistician} {\bf 56}, 121--130. (Andrew Gelman, Cristian Pasarica, and Rahul Dodhia)

• [2002] Prior distribution. {\em Encyclopedia of Environmetrics} {\bf 3}, 1634--1637. (Andrew Gelman)

• [2002] Posterior distribution. {\em Encyclopedia of Environmetrics} {\bf 3}, 1627--1628. (Andrew Gelman)

• [2002] Voting, fairness, and political representation (with discussion). {\em Chance} {\bf 15} (3), 22--26. (Andrew Gelman)

• [2002] You can load a die but you can't bias a coin. {\em American Statistician} {\bf 56}, 308--311. (Andrew Gelman and Deborah Nolan)

• [2002] Some statistical sampling and data collection activities. {\em Mathematics Teacher} {\bf 95}, 688--693. (Andrew Gelman and Deborah Nolan)

• [2002] A class project in survey sampling. {\em College Teaching} {\bf 50}, 151--153. (Andrew Gelman and Deborah Nolan)

• [2002] A probability model for golf putting. {\em Teaching Statistics} {\bf 50}, 151--153. (Andrew Gelman and Deborah Nolan)

• [2001] Using conditional distributions for missing-data imputation. Discussion of ``Conditionally specified distributions'' by B. Arnold et al. {\em Statistical Science} {\bf 3}, 268--269. (Andrew Gelman and T. E. Raghunathan)

• [2001] Bayesian inference with probability matrix decomposition models. {\em Journal of Educational and Behavioral Statistics} {\bf 26}, 153--179. (Michel Meulders, Paul De Boeck, Iven Van Mechelen, Andrew Gelman, and Eric Maris)

• [2001] Alternative models for longitudinal binary outcome data: A case study on their choice, interpretation and checking. {\em Biostatistics} {\bf 2}, 397--416. (John B. Carlin, C. Hendricks Brown, Andrew Gelman, and Rory Wolfe)

• [2001] Analysis of large-scale social surveys. {\em International Encyclopedia of Social and Behavioral Sciences}, ed.\ N. J. Smelser and P. B. Baltes, 8386--8392. Oxford University Press. (Elaine Zanutto and Andrew Gelman)

• [2001] Post-stratification without population level information on the post-stratifying variable, with application to political polling. {\em Journal of the American Statistical Association} {\bf 96}, 1--11. (Cavan Reilly, Andrew Gelman, and Jonathan N. Katz)

• [2001] Models, assumptions, and model checking in ecological regressions. {\em Journal of the Royal Statistical Society A} {\bf 164}, 101--118. (Andrew Gelman, Stephen Ansolabehere, Phillip N. Price, David K. Park, and Lorraine C. Minnite)

• [2001] Poststratification and weighting adjustments. In {\em Survey Nonresponse}, ed.\ R. Groves, D. Dillman, J. Eltinge, and R. Little. (Andrew Gelman and John B. Carlin)

• [2000] Discussion of ``Inference in molecular population genetics,'' by M. Stephens and P. Donnelly. {\em Journal of the Royal Statistical Society B}. (Stephen Brooks and Andrew Gelman)

• [2000] Optimization and simulation transfer algorithms. Disucssion of ``Optimization transfer using surrogate objective functions,'' by Kenneth Lange, David R. Hunter, and Ilsoon Yang. {\em Journal of Computational and Graphical Statistics} {\bf 9}, 49--51. (Andrew Gelman)

• [2000] Simulation modeling for cost estimation. In {\em Current Directions in Postal Reform}, ed.\ M. A. Crew and P. R. Kleindorfer, 171--193. Boston: Kluwer. (Richard Waterman, Donald Rubin, Neal Thomas, and Andrew Gelman)

• [2000] Bayesiaanse variantieanalyse. {\em Kwantitative Methoden} {\bf 21}, 5--12. (Andrew Gelman)

• [2000] Should we take measurements at an intermediate design point? {\em Biostatistics} {\bf 1}, 27--34. (Andrew Gelman)

• [2000] A method for quantifying artifacts in mapping methods, illustrated by application to headbanging. {\em Statistics in Medicine} {\bf 19}, 2309--2320. (Andrew Gelman, Phillip N. Price, and Chia-yu Lin)

• [2000] Type S error rates for classical and Bayesian single and multiple comparison procedures. {\em Computational Statistics} {\bf 15}, 373--390. (Andrew Gelman and Francis Tuerlinckx)

• [2000] Bayesian probabilistic extensions of a deterministic classification model. {\em Computational Statistics}, {\bf 15}, 355--371. (Iwin Leenen, Iven Van Mechelen, and Andrew Gelman)

• [2000] Diagnostic checks for discrete-data regression models using posterior predictive simulations. {\em Journal of the Royal Statistical Society C, Applied Statistics} {\bf 49}, 247--268. (Andrew Gelman, Yuri Goegebeur, Francis Tuerlinckx, and Iven Van Mechelen)

• [2000] Some class-participation demonstrations for introductory probability and statistics. {\em Journal of Educational and Behavioral Statistics} {\bf 25}, 84--100. (Andrew Gelman and Mark Glickman)

• [1999] Analysis of local decisions using hierarchical modeling, applied to home radon measurement and remediation (with discussion). {\em Statistical Science}, {\bf 14}, 305--337. (Chia-Yu Lin, Andrew Gelman, Phillip N. Price, and David H. Krantz)

• [1999] Optimal design for a study of butadiene toxicokinetics in humans. {\em Toxicological Sciences} {\bf 49}, 213--224. (Frederic Y. Bois, Thomas J. Smith, Andrew Gelman, Ho-Yuan Chang, and Andrew E. Smith)

• [1999] Evaluating and using statistical methods in the social sciences. Discussion of ``A critique of the Bayesian information criterion,'' by D. Weakliem. {\em Sociological Methods and Research} {\bf 27}, 403--410. (Andrew Gelman and Donald B. Rubin)

• [1999] All maps of parameter estimates are misleading. {\em Statistics in Medicine} {\bf 18}, 3221--3234. (Andrew Gelman and Phillip N. Price)

• [1998] Discussion of ``Quantifying surprise in the data and model verification,'' by M. J. Bayarri and J. O. Berger. {\em Bayesian Statistics 6}, 75--76. (Xiao-Li Meng and Andrew Gelman)

• [1998] Discussion of ``Some algebra and geometry for hierarchical models, applied to diagnostics,'' by J. H. Hodges. {\em Journal of the Royal Statistical Society B} {\bf 60}, 532. (Andrew Gelman and Phillip N. Price)

• [1998] Discussion of ``Bayesian projection of the acquired immune deficiency syndrome epidemic,'' by D. De Angelis, W. R. Gilks, and N. E. Day. {\em Journal of the Royal Statistical Society B} {\bf 60}, 490--491. (Andrew Gelman and John B. Carlin)

• [1998] Some issues in monitoring convergence of iterative simulations. {\em Computing Science and Statistics}. (Stephen Brooks and Andrew Gelman)

• [1998] Improving upon probability weighting for household size. {\em Public Opinion Quarterly} {\bf 62}, 398--404. (Andrew Gelman and Thomas C. Little)

• [1998] Generalizing the probability matrix decomposition model: an example of Bayesian model checking and model expansion. In {\em Assumptions, Robustness, and Estimation Methods in Multivariate Modeling}, ed.\ J. Hox. (Michel Meulders, Andrew Gelman, Iven Van Mechelen, and Paul De Boeck)

• [1998] Simulating normalizing constants: From importance sampling to bridge sampling to path sampling. {\em Statistical Science}. (Andrew Gelman and Xiao-Li Meng)

• [1998] General methods for monitoring convergence of iterative simulations. {\em Journal of Computational and Graphical Statistics} {\bf 7}, 434--455. (Stephen Brooks and Andrew Gelman)

• [1998] Modeling differential nonresponse in sample surveys. {\em Sankhya} {\bf 60}, 101--126. (Thomas C. Little and Andrew Gelman)

• [1998] Not asked and not answered: Multiple imputation for multiple surveys (with discussion). {\em Journal of the American Statistical Association} {\bf 93}, 846--874. (Andrew Gelman, Gary King, and Chuanhai Liu)
  [1998] Rejoinder to discussion. (Andrew Gelman, Gary King, and Chuanhai Liu)

• [1998] Estimating the probability of events that have never occurred: When is your vote decisive? {\em Journal of the American Statistical Association} {\bf 93}, 1--9. (Andrew Gelman, Gary King, and John Boscardin)

• [1998] Some class-participation demonstrations for decision theory and Bayesian statistics. {\em American Statistician} {\bf 52}, 167--174. (Andrew Gelman)

• [1998] Student projects on statistical literacy and the media. {\em American Statistician} {\bf 52}, 160--166. (Andrew Gelman and Deborah Nolan, with Anna Men, Steve Warmerdam, and Michelle Bautista)

• [1998] Markov chain Monte Carlo in practice: A roundtable discussion. {\em American Statistician} {\bf 52}, 93--100. (Robert E. Kass, Bradley P. Carlin, Andrew Gelman, and Radford M. Neal)

• [1997] Discussion of ``Analysis of non-randomly censored ordered categorical longitudinal data from analgesic trials,'' by L. B. Sheiner, S. L. Beal, and A. Dunne. {\em Journal of the American Statistical Association} {\bf 92}, 1248--1250. (Andrew Gelman and Frederic Y. Bois)

• [1997] Discussion of ``The EM algorithm---an old folk-song sung to a fast new tune,'' by X. L. Meng and D. Van Dyk. {\em Journal of the Royal Statistical Society B} {\bf 59}, 554. (Andrew Gelman)

• [1997] Poststratification into many categories using hierarchical logistic regression. {\em Survey Methodology} {\bf 23}, 127--135. (Andrew Gelman and Thomas C. Little)

• [1997] How can statistical theory help with statistical practice? Example of a Bayesian analysis in toxicokinetics. In {\em Good Statistical Practice. Proceedings of the 12th International Workshop on Statistical Modelling}, ed.\ C. E. Minder and H. Friedl, 61--70. Wien: Austrian Statistical Society. (Andrew Gelman and Frederic Y. Bois)

• [1997] Weak convergence and optimal scaling of random walk Metropolis algorithms. {\em Annals of Applied Probability} {\bf 7}, 110--120. (Gareth O. Roberts, Andrew Gelman, and Walter R. Gilks)

• [1997] Walking to school and traffic exposure in Australian children. {\em Australian and New Zealand Journal of Public Health} {\bf 21}, 286--292. (John B. Carlin, Mark R. Stevenson, Ian Roberts, Catherine M. Bennett, Andrew Gelman, and Terry Nolan)

• [1997] Using exams for teaching concepts in probability and statistics. {\em Journal of Educational and Behavioral Statistics} {\bf 22}, 237--243. (Andrew Gelman)

• [1996] Discussion of ``Hierarchical generalized linear models,'' by Y. Lee and J. A. Nelder. {\em Journal of the Royal Statistical Society B} {\bf 58}, 668. (Andrew Gelman)

• [1996] Physiological pharmacokinetic analysis using population modeling and informative prior distributions. {\em Journal of the American Statistical Association} {\bf 91}, 1400--1412. (Andrew Gelman, Frederic Y. Bois, and Jiming Jiang)

• [1996] Bayesian analysis of election surveys and forecasts. In {\em Case Studies in Bayesian Statistics 3}, ed.\ C. Gatsonis, J. S. Hodges, R. E. Kass, and N. D. Singpurwalla. (Andrew Gelman)

• [1996] Markov chain Monte Carlo methods in biostatistics. {\em Statistical Methods in Medical Research} {\bf 5}, 339--355. (Andrew Gelman and Donald B. Rubin)

• [1996] Bayesian prediction of mean indoor radon concentrations for Minnesota counties. {\em Health Physics} {\bf 71}, 922--936. (Phillip N. Price, Anthony V. Nero, and Andrew Gelman)

• [1996] Population toxicokinetics of tetrachloroethylene. {\em Archives of Toxicology} {\bf 70}, 347--355. (Frederic Y. Bois, Andrew Gelman, Jiming Jiang, Don Maszle, and George Alexeef)

• [1996] Posterior predictive assessment of model fitness via realized discrepancies (with discussion). {\em Statistica Sinica} {\bf 6}, 733--807. (Andrew Gelman, Xiao-Li Meng, and Hal S. Stern)

• [1996] Advantages of conflictual redistricting. In {\em Fixing the Boundaries: Defining and Redefining Single-Member Electoral Districts}, ed.\ I. McLean and D. Butler. Aldershot, England: Dartmouth Publishing Company, 207--217. (Andrew Gelman and Gary King) [ASCII file]

• [1996] Bayesian model-building by pure thought: Some principles and examples. {\em Statistica Sinica} {\bf 6}, 215--232. (Andrew Gelman)

• [1996] Bayesian regression with parametric models for heteroscedasticity. {\em Advances in Econometrics} {\bf 11}, A87--109. (W. John Boscardin and Andrew Gelman)

• [1996] Efficient Metropolis jumping rules. In {\em Bayesian Statistics 5}, ed.\ J. Bernardo et al., 599--607. Oxford University Press. (Andrew Gelman, Gareth O. Roberts, and Walter R. Gilks)
  [2014] Correction notice. (Andrew Gelman)

• [1995] Review of {\em Handbook of Statistical Modeling for the Social and Behavioral Sciences}, ed.\ G. Arminger, C. C. Clogg, and M. E. Sobel. {\em Contemporary Sociology} {\bf 24} 712--714. (Andrew Gelman)

• [1995] Discussion of ``Fractional Bayes factors for model comparison,'' by A. O'Hagan. {\em Journal of the Royal Statistical Society B} {\bf 57}, 131. (Andrew Gelman and Xiao-Li Meng)

• [1995] Discussion of ``Assessment and propagation of model uncertainty,'' by D. Draper. {\em Journal of the Royal Statistical Society B} {\bf 57}, 83. (Andrew Gelman and Xiao-Li Meng)

• [1995] Avoiding model selection in Bayesian social research. {\em Sociological Methodology 1995}, 165--173. (Andrew Gelman and Donald B. Rubin)

• [1995] Pre-election survey methodology: Details from nine polling organizations, 1988 and 1992. {\em Public Opinion Quarterly} {\bf 59}, 98--132. (D. Stephen Voss, Andrew Gelman, and Gary King)

• [1995] Method of moments using Monte Carlo simulation. {\em Journal of Computational and Graphical Statistics} {\bf 3}, 36--54. (Andrew Gelman)

• [1995] Inference and monitoring convergence. In {\em Practical Markov Chain Monte Carlo}, ed.\ W. Gilks, S. Richardson, and D. Spiegelhalter, 131--143. London: Chapman and Hall. (Andrew Gelman)

• [1995] Model checking and model improvement. In {\em Practical Markov Chain Monte Carlo}, ed.\ W. Gilks, S. Richardson, and D. Spiegelhalter, 189--201. London: Chapman and Hall. (Andrew Gelman and Xiao-Li Meng)

• [1995] Racial fairness in legislative redistricting. In {\em Classifying by Race}, ed.\ P. E. Peterson, 85--110. Princeton University Press. (Gary King, John M. Bruce, and Andrew Gelman)

• [1994] Discussion of ``A probabilistic model for the spatial distribution of party support in multiparty elections,'' by S. Merrill. {\em Journal of the American Statistical Association} {\bf 89}, 1198. (Andrew Gelman)

• [1994] Discussion of ``Approximate Bayesian inference and the weighted likelihood bootstrap,'' by M. A. Newton and A. E. Raftery. {\em Journal of the Royal Statistical Society B} {\bf 56}, 37--38. (Andrew Gelman)

• [1994] Enhancing democracy through legislative redistricting. {\em American Political Science Review} {\bf 88}, 541--559. (Andrew Gelman and Gary King)

• [1994] Party competition and media messages in U.S. Presidential elections. In {\em The Parties Respond}, second edition, ed.\ L. S. Maisel, 255--295. Westview Press. (Andrew Gelman and Gary King)

• [1994] A unified model for evaluating electoral systems and redistricting plans. {\em American Journal of Political Science} {\bf 38}, 514--554. (Andrew Gelman and Gary King)

• [1993] Assessing uncertainty in backprojection. {\em Statistical Science} {\bf 8}, 104--106. (John B. Carlin and Andrew Gelman)

• [1993] Review of {\em Forecasting Elections}, by M. S. Lewis-Beck and T. W. Rice. {\em Public Opinion Quarterly} {\bf 57}, 119--121. (Andrew Gelman)

• [1993] Discussion of ``Bayesian computation via the Gibbs sampler and related Markov chain methods,'' by A. F. M. Smith and G. O. Roberts. {\em Journal of the Royal Statistical Society B} {\bf 55}, 73. (Andrew Gelman and Donald B. Rubin)

• [1993] Why are American Presidential election campaign polls so variable when votes are so predictable? {\em British Journal of Political Science} {\bf 23}, 409--451. (Andrew Gelman and Gary King)

• [1993] Characterizing a joint probability distribution by conditionals. {\em Journal of the Royal Statistical Society B} {\bf 55}, 185--188. (Andrew Gelman and T. P. Speed)
  [1999] Correction notice. {\em Journal of the Royal Statistical Society B} {\bf 61}, 483. (Andrew Gelman and T. P. Speed)

• [1992] Discussion of ``Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,'' by J. Geweke. In {\em Bayesian Statistics 4}, ed.\ J. Bernardo et al., 190. Oxford University Press. (Andrew Gelman and Donald B. Rubin)

• [1992] Discussion of ``Maximum entropy and the nearly black object,'' by D. L. Donoho et al. {\em Journal of the Royal Statistical Society B} {\bf 54}, 72--73. (Andrew Gelman)

• [1992] Inference from iterative simulation using multiple sequences (with discussion). {\em Statistical Science} {\bf 7}, 457--511. (Andrew Gelman and Donald B. Rubin)
  [1992] Replication without contrition (rejoinder to discussion). (Andrew Gelman and Donald B. Rubin)

• [1992] Iterative and non-iterative simulation algorithms. {\em Computing Science and Statistics} {\bf 24}, 433--438. (Andrew Gelman)

• [1992] A single series from the Gibbs sampler provides a false sense of security. In {\em Bayesian Statistics 4}, ed.\ J. Bernardo et al., 625--631. Oxford University Press. (Andrew Gelman and Donald B. Rubin)

• [1991] The precision of positron emission tomography: theory and measurement. {\em Journal of Cerebral Blood Flow and Metabolism} {\bf 11}, A26--30. (Nathaniel Alpert, W. C. Barker, A. Gelman, S. Weise, M. Senda, and J. A. Correia)

• [1991] Systemic consequences of incumbency advantage in U.S. House elections. {\em American Journal of Political Science} {\bf 35}, 110--138. (Gary King and Andrew Gelman)

• [1991] A note on bivariate distributions that are conditionally normal. {\em American Statistician} {\bf 45}, 125--126. (Andrew Gelman and Xiao-Li Meng)

• [1990] Topics in image reconstruction for emission tomography. Ph.D. thesis, Department of Statistics, Harvard University. (Andrew Gelman)

• [1990] Discussion of ``A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography,'' by B. W. Silverman et al. {\em Journal of the Royal Statistical Society B} {\bf 52}, 314--315. (Andrew Gelman)

• [1990] Estimating incumbency advantage without bias. {\em American Journal of Political Science} {\bf 34}, 1142--1164. (Andrew Gelman and Gary King)

• [1990] Estimating the electoral consequences of legislative redistricting. {\em Journal of the American Statistical Association} {\bf 85}, 274--282. (Andrew Gelman and Gary King)

• [1989] Electoral responsiveness in U.S. Congressional elections, 1946--1986 (abstract). {\em Proceedings of the Social Statistics Section, American Statistical Association}, 208. (Andrew Gelman and Gary King)

• [1989] Constrained maximum entropy methods in an image reconstruction problem. In {\em Maximum Entropy and Bayesian Methods}, ed.\ J. Skilling, 429--435. Kluwer Academic Publishers (Andrew Gelman)

• [1987] Subboundary-free zone-melt recrystallization of thin-film silicon. {\em Applied Physics Letters} {\bf 51}, 1256--1258. (Loren Pfeiffer, Andrew Gelman, K. A. Jackson, K. W. West, and J. L. Batstone)

• [1987] Growth mechanisms during thin film crystallization from the melt. {\em Materials Research Society Symposium Proceedings} {\bf 74}, 543--553. (Loren Pfeiffer, Andrew Gelman, K. A. Jackson, and K. W. West)

• [1986] Reduced subboundary misalignment in SOI films scanned at low velocities. {\em Materials Research Society Symposium Proceedings} {\bf 53}, 29--37. (Loren Pfeiffer, K. W. West, D. C. Joy, J. M. Gibson, and A. Gelman)

• [1986] Towards a better understanding of the peaceful society of First World War trenches. Undergraduate thesis, Department of Physics, Massachusetts Institute of Technology. (Andrew Gelman)

• [1984] The effects of solar flares on single event upset rates. {\em IEEE Transactions on Nuclear Science and Radiation Effects} {\bf NS-31}, 1212--1216. (James H. Adams, Jr., and Andrew Gelman)

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