Conjugate Bayes for probit regression via unified skew-normal distributions

TL;DR

This paper demonstrates that the posterior distribution for probit regression coefficients with Gaussian priors can be expressed as a unified skew-normal distribution, enabling more efficient Bayesian inference especially in high-dimensional, small-sample contexts.

Contribution

It proves the posterior for probit coefficients has a unified skew-normal kernel, facilitating computationally efficient Bayesian inference in complex, high-dimensional problems.

Findings

Posterior distribution is a unified skew-normal under Gaussian priors.

Efficient inference methods are developed for large p, small n settings.

Application demonstrated in a genetic study.

Abstract

Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve also as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference proceeds by updating the priors for the coefficients, typically set to be Gaussians, with the likelihood induced by probit or logit regressions for the responses. In this updating, the apparent absence of a tractable posterior has motivated a variety of computational methods, including Markov Chain Monte Carlo routines and algorithms which approximate the posterior. Despite being routinely implemented, Markov Chain Monte Carlo strategies face mixing or time-inefficiency issues in large p and small n studies, whereas approximate routines fail to capture the skewness typically observed in the posterior. This article proves that the posterior distribution for the probit coefficients has a unified skew-normal kernel, under Gaussian priors. Such a novel result allows efficient Bayesian inference for a wide class of applications, especially in large p and small-to-moderate n studies where state-of-the-art computational methods face notable issues. These advances are outlined in a genetic study, and further motivate the development of a wider class of conjugate priors for probit models along with methods to obtain independent and identically distributed samples from the unified skew-normal posterior.