Please read our student and staff community guidance on COVID-19
Skip to main content Skip to navigation

Paper No. 09-41

Download 09-41

D Lamnisos, JE Griffin and MFJ Steel

Adaptive Monte Carlo for Binary Regression with Many Regressors

Abstract: This article describes a method for efficient posterior simulation for Bayesian variable selection in probit regression models with many regressors but few observations. A proposal on model space is described which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, efficient computation in these models. The methods is applied to the analysis of gene expression data.