Bayesian inference for semiparametric binary regression

Michael A. Newton, Rick Chappell, Claudia Czado

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

We propose a regression model for binary response data that places no structural restrictions on the link function except monotonicity and known location and scale. Predictors enter linearly. We demonstrate Bayesian inference calculations in this model. By modifying the Dirichlet process, we obtain a natural prior measure over this semiparametric model, and we use Polya sequence theory to formulate this measure in terms of a finite number of unobserved variables. We design a Markov chain Monte Carlo algorithm for posterior simulation and apply the methodology to data on radiotherapy treatments for cancer.

Original languageEnglish
Pages (from-to)142-153
Number of pages12
JournalJournal of the American Statistical Association
Volume91
Issue number433
DOIs
StatePublished - 1 Mar 1996
Externally publishedYes

Keywords

  • Dirichlet process
  • Latent variables
  • Link function
  • Logistic regression
  • Markov chain Monte Carlo
  • Polya sequence

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