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Mathematics
USA
2026

D-Index & Metrics

Mathematics

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

Andrew Gelman publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Andrew Gelman sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 510 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Andrew Gelman D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Andrew Gelman sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 118 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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