Standout Papers

Inference from Iterative Simulation Using Multiple Sequences 1992 2026 2003 2014 10.9k
  1. Inference from Iterative Simulation Using Multiple Sequences (1992)
    Andrew Gelman, Donald B. Rubin Statistical Science
  2. Bayesian Data Analysis (1995)
    Andrew Gelman, John B. Carlin et al. CERN Document Server (European Organization for Nuclear Research)
  3. Data Analysis Using Regression and Multilevel/Hierarchical Models (2006)
    Andrew Gelman, Jennifer Hill Cambridge University Press eBooks
  4. General Methods for Monitoring Convergence of Iterative Simulations (1998)
    Stephen P. Brooks, Andrew Gelman Journal of Computational and Graphical Statistics
  5. Stan: A Probabilistic Programming Language (2017)
    Bob Carpenter, Andrew Gelman et al. Journal of Statistical Software
  6. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2016)
    Aki Vehtari, Andrew Gelman et al. Statistics and Computing
  7. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) (2006)
    Andrew Gelman Bayesian Analysis
  8. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo (2014)
    Andrew Gelman et al. arXiv (Cornell University)
  9. Scaling regression inputs by dividing by two standard deviations (2007)
    Andrew Gelman Statistics in Medicine
  10. Handbook of Markov Chain Monte Carlo (2011)
    Steve Brooks, Andrew Gelman et al. arXiv (Cornell University)
  11. General Methods for Monitoring Convergence of Iterative Simulations (1998)
    Stephen P. Brooks, Andrew Gelman Journal of Computational and Graphical Statistics
  12. Understanding predictive information criteria for Bayesian models (2013)
    Andrew Gelman, Jessica Hwang et al. Statistics and Computing
  13. Beyond Power Calculations (2014)
    Andrew Gelman, John B. Carlin Perspectives on Psychological Science
  14. Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs (2017)
    Andrew Gelman, Guido W. Imbens Journal of Business and Economic Statistics
  15. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant (2006)
    Andrew Gelman, Hal S. Stern The American Statistician
  16. Increasing Transparency Through a Multiverse Analysis (2016)
    Sara Steegen, Francis Tuerlinckx et al. Perspectives on Psychological Science
  17. Simulating normalizing constants: from importance sampling to bridge sampling to path sampling (1998)
    Andrew Gelman, Xiao‐Li Meng Statistical Science
  18. Bayesian statistics and modelling (2021)
    Rens van de Schoot, Sarah Depaoli et al. Nature Reviews Methods Primers
  19. Visualization in Bayesian Workflow (2019)
    Jonah Gabry, Daniel Simpson et al. Journal of the Royal Statistical Society Series A (Statistics in Society)
  20. An Analysis of the New York City Police Department's “Stop-and-Frisk” Policy in the Context of Claims of Racial Bias (2007)
    Andrew Gelman, Jeffrey Fagan et al. Journal of the American Statistical Association
  21. The Statistical Crisis in Science (2014)
    Andrew Gelman, Eric Loken American Scientist
  22. R-squared for Bayesian Regression Models (2018)
    Andrew Gelman, Ben Goodrich et al. The American Statistician
  23. Partisans without Constraint: Political Polarization and Trends in American Public Opinion (2008)
    Delia Baldassarri, Andrew Gelman American Journal of Sociology
  24. Measurement error and the replication crisis (2017)
    Eric Loken, Andrew Gelman Science
  25. A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models (2013)
    Yeojin Chung, Sophia Rabe‐Hesketh et al. Psychometrika
  26. Stan (2015)
    Andrew Gelman, Daniel Lee et al. Journal of Educational and Behavioral Statistics
  27. Global shifts in the phenological synchrony of species interactions over recent decades (2018)
    Heather M. Kharouba, Johan Ehrlén et al. Proceedings of the National Academy of Sciences
  28. Regression and Other Stories (2020)
    Andrew Gelman, Jennifer Hill et al. Cambridge University Press eBooks
  29. The Prior Can Often Only Be Understood in the Context of the Likelihood (2017)
    Andrew Gelman, Daniel Simpson et al. Columbia Academic Commons (Columbia University)

Immediate Impact

1 by Nobel laureates 39 from Science/Nature 69 standout
Sub-graph 1 of 22

Citing Papers

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6 intermediate papers

Works of Andrew Gelman being referenced

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
2016 Standout
Understanding predictive information criteria for Bayesian models
2013 Standout
and 4 more

Author Peers

Author Last Decade Papers Cites
Andrew Gelman 11993 8358 6499 8702 334 70.9k
Eric R. Ziegel 8103 5929 3627 3539 590 74.4k
Trevor Hastie 18864 24188 2313 12491 272 152.2k
Robert Tibshirani 24081 26631 2652 6490 380 181.1k
Adrian E. Raftery 8141 9867 3702 2378 251 51.6k
Jerome H. Friedman 12811 27492 2285 7323 126 121.1k
Leo Breiman 7521 33402 2497 14638 91 136.3k
George E. P. Box 11228 6924 1755 2337 269 82.1k
Donald B. Rubin 52268 21131 18513 3123 384 184.2k
Jacob Cohen 3635 4883 9514 2678 103 89.6k
Hirotugu Akaike 4059 4744 1517 4141 77 48.4k

All Works

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2026