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Bayesian inference for semiparametric binary regression

delete1996-03-01
delete64
PRE
AI
M
Michael A. Newton
C
Claudia Czado
R
Rick Chappell
DOI：10.2307/2291390delete
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Abstract

Abstract

En 中文
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.
Keywords:
Dirichlet process
latent variables
link function
logistic regression
Markov chain Monte Carlo
Polya sequence

Journal

J
Journal of the American Statistical Association
IF:
3
Papers:
5.1K
Citations:
4.8W

Organization

No organization information available