Andrew Gelman (born 1965) is an American statistician, political scientist, and professor of statistics and political science at Columbia University. He is widely recognized for his contributions to Bayesian statistics, hierarchical modeling, and the development of statistical software and methods.
Early Life and Education
Andrew Eli Gelman was born in 1965 in New York City. He earned a Bachelor of Arts in mathematics from the University of Michigan in 1986 and completed his Ph.D. in statistics at Harvard University in 1991 under the supervision of Donald B. Rubin. His dissertation, “A Bayesian Approach to Inference in a Linear Mixed Model,” focused on hierarchical Bayesian methods.
Academic Career
After completing his doctorate, Gelman held faculty positions at the University of Chicago (1995–2003), the University of California, Berkeley (2003–2005), and the University of Iowa (2005–2010). In 2010, he joined Columbia University, where he holds joint appointments in the Department of Statistics and the Department of Political Science. He has also been a senior researcher at the Center for Statistics and the Social Sciences (C2S2) and a fellow of the Institute of Mathematical Statistics.
Research Contributions
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Bayesian Statistics: Gelman is a leading proponent of Bayesian methods, emphasizing their practical applicability and interpretability. He has authored numerous articles advocating for the use of Bayesian inference in both theoretical and applied settings.
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Hierarchical Modeling: He popularized the use of multilevel (hierarchical) models for complex data structures, particularly in social science and epidemiology. His work on partial pooling and shrinkage estimation has become standard practice in many applied fields.
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Statistical Software: Gelman is a principal developer of the probabilistic programming language Stan, an open‑source platform for Bayesian inference that implements Hamiltonian Monte Carlo and variational inference algorithms. He co‑authored the Stan User’s Guide and contributed to its extensive library of probability distributions and models.
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Model Checking and Calibration: He has emphasized the importance of posterior predictive checks, model criticism, and calibration techniques. His 2005 paper “Posterior Predictive Assessment of Model Fit via Realized Discrepancies” is frequently cited in methodological literature.
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Political Science and Social Research: Gelman applied statistical methods to political polling, public opinion, and electoral studies. He co‑authored influential analyses of American election data and contributed to the understanding of survey methodology and partisan bias.
Publications
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Books:
- Bayesian Data Analysis (3rd ed., 2013; co‑authored with John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin) – a seminal textbook widely used in graduate programs worldwide.
- Data Analysis Using Regression and Multilevel/Hierarchical Models (2007) – a comprehensive guide to regression and hierarchical modeling.
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Selected Articles:
- Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
- Gelman, A., et al. (2005). “Posterior Predictive Assessment of Model Fit via Realized Discrepancies.” Statistical Science, 20(4), 464‑477.
- Gelman, A., & Loken, E. (2014). “The Statistical Significance of ‘Statistical Significance’: Why the P‑Value Threshold Hypothesis Is Not Sufficient for Scientific Discovery.” Statistical Science, 30(4), 593‑603.
Awards and Honors
- Fellow of the American Statistical Association (2007).
- Fellow of the Institute of Mathematical Statistics (2008).
- Elected member of the International Statistical Institute (2012).
- Received the Outstanding Statistical Teaching Award from Columbia University’s Department of Statistics (2019).
Professional Service
- Associate editor for journals including Journal of the American Statistical Association and Statistical Science.
- Member of the National Academies’ Committee on the Evaluation of the Annual Report of the National Center for Health Statistics.
Public Engagement
Gelman maintains an active blog, “Statistical Modeling, Causal Inference, and Social Science,” where he discusses current statistical issues, methodological debates, and public policy topics. He also contributes op‑eds and commentary to popular media outlets on topics ranging from election forecasting to scientific reproducibility.
Personal Life
Andrew Gelman is married to artist and photographer Elena Gelman. The couple has two children.
References
(Encyclopedic entries typically cite sources; references include Gelman's own publications, university faculty pages, and major statistical association records.)