| Bayesian data analysis, 3rd edition A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin Chapman & Hall/CRC, 2013 | 36123* | 2013 |
| Data analysis using regression and multilevel/hierarchical models A Gelman, J Hill Cambridge university press, 2006 | 17276 | 2006 |
| Inference from iterative simulation using multiple sequences A Gelman, DB Rubin Statistical science, 457-472, 1992 | 16914 | 1992 |
| General methods for monitoring convergence of iterative simulations SP Brooks, A Gelman Journal of computational and graphical statistics 7 (4), 434-455, 1998 | 7204 | 1998 |
| Stan: A probabilistic programming language B Carpenter, A Gelman, MD Hoffman, D Lee, B Goodrich, M Betancourt, ... Journal of statistical software 76, 2017 | 6510 | 2017 |
| Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) A Gelman | 5052 | 2006 |
| The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. MD Hoffman, A Gelman J. Mach. Learn. Res. 15 (1), 1593-1623, 2014 | 4301 | 2014 |
| Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC A Vehtari, A Gelman, J Gabry Statistics and computing 27, 1413-1432, 2017 | 3661 | 2017 |
| Posterior predictive assessment of model fitness via realized discrepancies A Gelman, XL Meng, H Stern Statistica sinica, 733-760, 1996 | 2781 | 1996 |
| Handbook of markov chain monte carlo S Brooks, A Gelman, G Jones, XL Meng CRC press, 2011 | 2621 | 2011 |
| Scaling regression inputs by dividing by two standard deviations A Gelman Statistics in medicine 27 (15), 2865-2873, 2008 | 2273 | 2008 |
| Weak convergence and optimal scaling of random walk Metropolis algorithms A Gelman, WR Gilks, GO Roberts The annals of applied probability 7 (1), 110-120, 1997 | 2234 | 1997 |
| A weakly informative default prior distribution for logistic and other regression models A Gelman, A Jakulin, MG Pittau, YS Su | 2033 | 2008 |
| R2WinBUGS: a package for running WinBUGS from R S Sturtz, U Ligges, A Gelman Journal of Statistical software 12, 1-16, 2005 | 1953 | 2005 |
| Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014 | 1924 | 2014 |
| Why high-order polynomials should not be used in regression discontinuity designs A Gelman, G Imbens Journal of Business & Economic Statistics 37 (3), 447-456, 2019 | 1733 | 2019 |
| Efficient Metropolis jumping rules A Gelman, GO Roberts, WR Gilks Bayesian statistics 5 (599-608), 42, 1996 | 1481 | 1996 |
| Why we (usually) don't have to worry about multiple comparisons A Gelman, J Hill, M Yajima Journal of research on educational effectiveness 5 (2), 189-211, 2012 | 1316 | 2012 |
| Why are American presidential election campaign polls so variable when votes are so predictable? A Gelman, G King British Journal of Political Science 23 (4), 409-451, 1993 | 1241 | 1993 |
| Simulating normalizing constants: From importance sampling to bridge sampling to path sampling A Gelman, XL Meng Statistical science, 163-185, 1998 | 1238 | 1998 |
