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Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
118
Citations
190966
World Ranking
15
National Ranking
10

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2020 - Fellow of the American Academy of Arts and Sciences
  • 2003 - COPSS Presidents' Award
  • 1998 - Fellow of the American Statistical Association (ASA)

Overview

Andrew Gelman is affiliated with Columbia University in the United States. Their research focuses primarily on mathematics, with a significant number of publications in the field of statistics and probability. They have explored various subfields, including artificial intelligence, sociology and political science, infectious diseases, and statistics, probability, and uncertainty. The main topics of their work include statistical methods and inference, statistical methods and Bayesian inference, advanced causal inference techniques, COVID-19 epidemiological studies, meta-analysis and systematic reviews, Bayesian methods and mixture models, and statistical methods in clinical trials.

Gelman has authored multiple papers published in various notable venues. Recent publications include:

  • Bayesian statistics and modelling, 2021, Nature Reviews Methods Primers
  • Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey, 2020, The Lancet Public Health
  • Bayesian Analysis of Tests with Unknown Specificity and Sensitivity, 2020, Journal of the Royal Statistical Society Series C (Applied Statistics)
  • No reason to expect large and consistent effects of nudge interventions, 2022, Proceedings of the National Academy of Sciences
  • Bayesian Workflow, 2020, arXiv (Cornell University)

Their frequent coauthors include Aki Vehtari, Jennifer Hill, Philip Greengard, Jessica Hullman, and Yuling Yao. Gelman's works have been published extensively in venues such as arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), CHANCE, Harvard Data Science Review, and Nature Reviews Methods Primers.

In addition to journal articles, Gelman has contributed to academic book publications. They have published two books through Cambridge University Press:

  • Regression and Other Stories, 2020
  • Active Statistics, 2024

Their contributions to the scientific community have been recognized through several awards. They were named a Fellow of the American Academy of Arts and Sciences in 2020 and a Fellow of the American Statistical Association in 1998. In 2003, Gelman received the COPSS Presidents' Award.

Best Publications

  • Bayesian Data Analysis

    Andrew Gelman;John B. Carlin;Hal S. Stern;David B. Dunson

  • Inference from Iterative Simulation Using Multiple Sequences

    Andrew Gelman;Donald B. Rubin

  • Data Analysis Using Regression and Multilevel/Hierarchical Models

    Andrew Gelman;Yu-Sung Su

  • Stan: A Probabilistic Programming Language

    Bob Carpenter;Andrew Gelman;Matthew D. Hoffman;Daniel Lee

  • General methods for monitoring convergence of iterative simulations

    Stephen P. Brooks;Andrew Gelman

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

    Andrew Gelman

  • Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

    Aki Vehtari;Andrew Gelman;Jonah Gabry

  • The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo

    Matthew D. Homan;Andrew Gelman

  • Handbook of Markov Chain Monte Carlo

    Steve Brooks;Andrew Gelman;Galin L. Jones;Xiao-Li Meng

  • POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES

    Andrew Gelman;Xiao-Li Meng;Hal Stern

  • Scaling regression inputs by dividing by two standard deviations.

    Andrew Gelman

  • Prior distributions for variance parameters in hierarchical models

    Andrew Gelman

  • A weakly informative default prior distribution for logistic and other regression models

    Andrew Gelman;Aleks Jakulin;Maria Grazia Pittau;Yu Sung Su

  • Understanding predictive information criteria for Bayesian models

    Andrew Gelman;Jessica Hwang;Aki Vehtari

  • Weak convergence and optimal scaling of random walk Metropolis algorithms

    G. O. Roberts;A. Gelman;W. R. Gilks

  • Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs

    Andrew Gelman;Guido Imbens

  • R2WinBUGS: A Package for Running WinBUGS from R

    Sibylle Sturtz;Uwe Ligges;Andrew E. Gelman

  • Why we (usually) don't have to worry about multiple comparisons

    Andrew E. Gelman;Jennifer Hill;Masanao Yajima

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

    Andrew Gelman;John Carlin

  • The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

    Matthew D. Hoffman;Andrew Gelman

  • Bayesian data analysis, third edition

    A Gelman;JB Carlin;HS Stern;DB Dunson

  • Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417

    Steve Brooks;Andrew Gelman;Galin Jones;Xiao-Li Meng

  • Appears as Chapter 5 of the Handbook of Markov Chain Monte Carlo

    Steve Brooks;Andrew Gelman;Galin Jones;Xiao-Li Meng

Frequent Co-Authors

Aki Vehtari
Aki Vehtari Aalto University
Donald B. Rubin
Donald B. Rubin Temple University
John B. Carlin
John B. Carlin University of Melbourne
Christian P. Robert
Christian P. Robert Paris Dauphine University
David B. Dunson
David B. Dunson Duke University
Gary King
Gary King Harvard University
Eric-Jan Wagenmakers
Eric-Jan Wagenmakers University of Amsterdam
Daniel J. Lee
Daniel J. Lee Samsung (South Korea)
Shravan Vasishth
Shravan Vasishth University of Potsdam
Dustin Tran
Dustin Tran Google (United States)

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