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Andrew Gelman works on Bayesian statistics, statistical modelling and the interpretation of data in scientific and social questions. His research and teaching connect hierarchical Bayesian models, regression, computation and statistical graphics. His Columbia research page describes applications ranging from survey inference and elections to environmental and medical measurements.^[ref]

A characteristic modelling problem is learning about many related groups: completely separate estimates can be noisy, while a single common estimate can hide differences. A hierarchical Bayesian model estimates group parameters together and lets the data influence their degree of pooling. Model checking, including posterior predictive checks, then asks whether simulated observations reproduce scientifically relevant features of the actual data. This connects fitting a model with examining the adequacy of its assumptions.

By Andrew Gelman, John Carlin, Hal S. Stern, David Dunson, Aki Vehtari and Donald Rubin, as credited on the author-hosted book homepage. Third edition.

Official homepage: sites.stat.columbia.edu/gelman/book/

The homepage supplies the author list and an authorized PDF for noncommercial use. Cited here for the coauthorship of Hal S. Stern and the textbook’s Bayesian subject.

Hal S. Stern is a statistician whose research includes Bayesian statistics and the analysis of forensic evidence. He is a coauthor of Bayesian Data Analysis by Andrew Gelman and others and of the article with Andrew Gelman explaining why a difference between statistical significance and nonsignificance need not itself be statistically significant. This distinction separates the comparison of two estimated effects from two separate hypothesis tests.^[ref]