Andrew Gelman
Affiliations: | Columbia University, New York, NY |
Area:
StatisticsWebsite:
http://www.stat.columbia.edu/~gelman/Google:
"Andrew Gelman"Parents
Sign in to add mentor| Donald B. Rubin | grad student | 1990 | Harvard | |
| (Topics in image reconstruction for emission tomography) | ||||
Children
Sign in to add trainee| Joseph L Sutherland | grad student | (PoliSci Tree) | |
| Cavan S. Reilly | grad student | 2000 | Columbia |
| Hao Lu | grad student | 2001 | Columbia |
| Zaiying Huang | grad student | 2004 | Columbia |
| Alexander Kiss | grad student | 2004 | Columbia |
| Cristian Pasarica | grad student | 2004 | Columbia |
| Jouni P. Kerman | grad student | 2006 | Columbia |
| Rachel Schutt | grad student | 2010 | Columbia |
| Cyrus Samii | grad student | 2011 | Columbia (PoliSci Tree) |
| Vincent J. Dorie | grad student | 2014 | Columbia |
| Yair Ghitza | grad student | 2014 | Columbia |
BETA: Related publications
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Publications
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| Aczel B, Hoekstra R, Gelman A, et al. (2020) Discussion points for Bayesian inference. Nature Human Behaviour |
| Gao Y, Kennedy L, Simpson D, et al. (2020) Improving Multilevel Regression and Poststratification with Structured Priors Bayesian Analysis |
| Gelman A, Carpenter B. (2020) Bayesian analysis of tests with unknown specificity and sensitivity Journal of the Royal Statistical Society Series C-Applied Statistics |
| Gelman A, Guzey A. (2020) Statistics as Squid Ink: How Prominent Researchers Can Get Away with Misrepresenting Data Chance. 33: 25-27 |
| Ghitza Y, Gelman A. (2020) Voter Registration Databases and MRP: Toward the Use of Large-Scale Databases in Public Opinion Research Political Analysis. 1-25 |
| Morris M, Wheeler-Martin K, Simpson D, et al. (2019) Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan. Spatial and Spatio-Temporal Epidemiology. 31: 100301 |
| Gelman A. (2019) When we make recommendations for scientific practice, we are (at best) acting as social scientists. European Journal of Clinical Investigation. e13165 |
| Gelman A. (2019) Of chaos, storms and forking paths: the principles of uncertainty. Nature. 569: 628-629 |
| Gabry J, Simpson D, Vehtari A, et al. (2019) Visualization in Bayesian workflow Journal of the Royal Statistical Society Series a-Statistics in Society. 182: 389-402 |
| van Dongen NNN, van Doorn JB, Gronau QF, et al. (2019) Multiple Perspectives on Inference for Two Simple Statistical Scenarios The American Statistician. 73: 328-339 |