Andrew Gelman
Andrew Gelman
Professor of Statistics and Political Science, Columbia University
Verified email at stat.columbia.edu - Homepage
TitleCited byYear
Bayesian data analysis, 3rd edition
A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin
Chapman & Hall/CRC, 2013
258702013
Data analysis using regression and multilevel/hierarchical models
A Gelman, J Hill
Cambridge university press, 2006
115752006
Inference from iterative simulation using multiple sequences
A Gelman, DB Rubin
Statistical science 7 (4), 457-472, 1992
110321992
General methods for monitoring convergence of iterative simulations
SP Brooks, A Gelman
Journal of computational and graphical statistics 7 (4), 434-455, 1998
47241998
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
A Gelman
Bayesian analysis 1 (3), 515-534, 2006
34392006
Posterior predictive assessment of model fitness via realized discrepancies
A Gelman, XL Meng, H Stern
Statistica sinica, 733-760, 1996
20511996
Stan: A probabilistic programming language
B Carpenter, A Gelman, MD Hoffman, D Lee, B Goodrich, M Betancourt, ...
Journal of statistical software 76 (1), 2017
20032017
Weak convergence and optimal scaling of random walk Metropolis algorithms
GO Roberts, A Gelman, WR Gilks
The annals of applied probability 7 (1), 110-120, 1997
15491997
R2WinBUGS: a package for running WinBUGS from R
S Sturtz, U Ligges, AE Gelman
15132005
The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.
MD Hoffman, A Gelman
Journal of Machine Learning Research 15 (1), 1593-1623, 2014
14512014
Handbook of markov chain monte carlo
S Brooks, A Gelman, G Jones, XL Meng
CRC press, 2011
14122011
Scaling regression inputs by dividing by two standard deviations
A Gelman
Statistics in medicine 27 (15), 2865-2873, 2008
12732008
Efficient Metropolis jumping rules
A Gelman, GO Roberts, WR Gilks
Bayesian statistics 5 (599-608), 42, 1996
11591996
A weakly informative default prior distribution for logistic and other regression models
A Gelman, A Jakulin, MG Pittau, YS Su
The Annals of Applied Statistics 2 (4), 1360-1383, 2008
11462008
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
10211993
Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
A Gelman, XL Meng
Statistical science, 163-185, 1998
9651998
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
A Vehtari, A Gelman, J Gabry
Statistics and computing 27 (5), 1413-1432, 2017
8552017
Understanding predictive information criteria for Bayesian models
A Gelman, J Hwang, A Vehtari
Statistics and computing 24 (6), 997-1016, 2014
7992014
Estimating incumbency advantage without bias
A Gelman, G King
Available at SSRN 1084180, 1990
7791990
The difference between “significant” and “not significant” is not itself statistically significant
A Gelman, H Stern
The American Statistician 60 (4), 328-331, 2006
7682006
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