Vol. 13, No. 1, 2020

Download this article
Download this article For screen
For printing
Recent Issues

Volume 13
Issue 2, 181–360
Issue 1, 1–180

Volume 12, 8 issues

Volume 11, 5 issues

Volume 10, 5 issues

Volume 9, 5 issues

Volume 8, 5 issues

Volume 7, 6 issues

Volume 6, 4 issues

Volume 5, 4 issues

Volume 4, 4 issues

Volume 3, 4 issues

Volume 2, 5 issues

Volume 1, 2 issues

The Journal
About the Journal
Editorial Board
Subscriptions
Editors’ Interests
Scientific Advantages
Submission Guidelines
Submission Form
Ethics Statement
Editorial Login
ISSN: 1944-4184 (e-only)
ISSN: 1944-4176 (print)
Author Index
Coming Soon
 
Other MSP Journals
A new go-to sampler for Bayesian probit regression

Scott Simmons, Elizabeth J. McGuffey and Douglas VanDerwerken

Vol. 13 (2020), No. 1, 77–89
Abstract

This paper introduces an independent-proposal Metropolis–Hastings sampler for Bayesian probit regression. The Gibbs sampler of Albert and Chib has been the default sampler since its introduction in 1993. We conduct a simulation study comparing the two methods under various combinations of predictor variables and sample sizes. The proposed sampler is shown to outperform that of Albert and Chib in terms of efficiency measured through autocorrelation, effective sample size, and computation time. We then demonstrate performance of the samplers on real data applications with analogous results.

Keywords
probit regression, Markov chain Monte Carlo, Gibbs sampling, Metropolis–Hastings
Mathematical Subject Classification 2010
Primary: 62C10, 62F15
Milestones
Received: 28 January 2019
Accepted: 14 November 2019
Published: 4 February 2020

Communicated by Jem Noelle Corcoran
Authors
Scott Simmons
US Naval Academy
Annapolis, MD
United States
Elizabeth J. McGuffey
US Naval Academy
Annapolis, MD
United States
Douglas VanDerwerken
US Naval Academy
Annapolis, MD
United States