A Multiple Comparison Procedure Based on a Variant of the Schwarz Information Criterion in a Mixed Model

• Activity Number: 506
• Type: Topic Contributed
• Date/Time: Wednesday, August 5, 2009 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Junfeng Shang*+
• Companies: Bowling Green State University
• Keywords: SIC variant ; posterior probability ; mixed model ; repeated measurements ; model selection

Abstract

In a mixed model for repeated measurements, we propose a multiple comparison procedure based on a variant of the Schwarz information criterion (SIC, Schwarz, 1978). Based upon the proposed variant, the corresponding posterior probability of a candidate model can be calculated. A hypothesis testing for multiple comparisons involves one or more models in the candidate class, and the posterior probability of a testing is therefore evaluated as the sum of the posterior probabilities for the models associated with the testing. The approximated posterior probability based on the variant accommodates the effect of the prior on a model, and therefore is more effectively approximated than that based on SIC for conducting multiple comparisons. We derive the computational formula of the approximated posterior probability based on the variant in the mixed model and apply it in two real data sets.