Bayesian Inference for Categorical Data Analysis

  • Agresti A
  • Hitchcock D
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Abstract

This article surveys Bayesian methods for categorical data analysis, with primary em-phasis on contingency table analysis. Early innovations were proposed by Good (1953, 1956, 1965) for smoothing proportions in contingency tables and by Lindley (1964) for inference about odds ratios. These approaches primarily used conjugate beta and Dirichlet priors. Altham (1969, 1971) presented Bayesian analogs of small-sample frequentist tests for 2×2 tables using such priors. An alternative approach using normal priors for logits received considerable attention in the 1970s by Leonard and others (e.g., Leonard 1972). Adopted usually in a hierarchical form, the logit-normal approach allows greater flexibility and scope for generalization. The 1970s also saw considerable interest in loglinear modeling. The ad-vent of modern computational methods since the mid-1980s has led to a growing literature on fully Bayesian analyses with models for categorical data, with main emphasis on general-ized linear models such as logistic regression for binary and multi-category response variables.

Author-supplied keywords

  • Beta distribution
  • Binomial distribution
  • Dirichlet distribution
  • Empirical Bayes
  • Graphical models
  • Hierarchical models
  • Logistic regression
  • Loglinear models
  • Markov chain Monte Carlo
  • Matched pairs
  • Multinomial distribution
  • Odds ratio
  • Smoothing 2

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Authors

  • Alan Agresti

  • David B Hitchcock

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