Bayesian Binomial Regression: Predicting Survival at a Trauma Center

Edward J. Bedrick, Ronald Christensen, Wesley Johnson

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods can be performed using simple computations, and they apply for any sample size. We provide a relatively complete discussion of Bayesian inferences for binomial regression with emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among nonnested models, and examine the sensitivity of the Bayesian methods.

Original languageEnglish (US)
Pages (from-to)211-218
Number of pages8
JournalAmerican Statistician
Volume51
Issue number3
DOIs
StatePublished - Aug 1997
Externally publishedYes

Keywords

  • Bayesian analysis
  • Importance sampling
  • Kullback–Leibler divergence
  • Logistic regression
  • Model selection
  • Prediction
  • Probit analysis
  • Regression diagnostics

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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