JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 147
Type: Invited
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303480
Title: Calibrated Bayes Factors for Model Comparison
Author(s): Xinyi Xu*+ and Pingbo Lu and Steven MacEachern and Ruoxi Xu
Companies: The Ohio State University and The Ohio State University and The Ohio State University and The Ohio State University
Address: 1958 Neil Ave., Columbus, OH, 43210,
Keywords: Prior elicitation ; Training sample ; Predictive performance ; Information level ; Unit information prior

Bayes factor is a widely used tool for Bayesian hypothesis testing and model comparison. However, it can be greatly affected by the prior elicitation for the model parameters. When the prior information is weak, people often use proper priors with large variances. In this work, we show that Bayes factors under convenient diffuse priors can be misleading when the models under comparisons differ in dimensions. Therefore, we propose an innovative method called calibrated Bayes factor, which uses data to calibrate the prior distributions before computing Bayes factors. We show that this method provides reliable and robust model preferences under various true models. It makes no assumption on model forms (parametric or nonparametric) or on the integrability of priors (proper or improper), so is applicable in a large variety of model comparison problems.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.