## Bayesian Inference for Semiparametric Binary Regression (1996)

Venue: | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION |

Citations: | 24 - 2 self |

### BibTeX

@ARTICLE{Newton96bayesianinference,

author = {Michael A. Newton and Claudia Czado and Rick Chappell},

title = {Bayesian Inference for Semiparametric Binary Regression},

journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},

year = {1996},

volume = {91},

pages = {142--153}

}

### OpenURL

### Abstract

We propose a regression model for binary response data which 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. A Markov chain Monte Carlo algorithm is designed for posterior simulation, and the methodology is applied to data on radiotherapy treatments for cancer.