Abstract #301189

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JSM 2003 Abstract #301189
Activity Number: 254
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 12:00 PM to 1:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301189
Title: Robust Bayesian Factor Analysis
Author(s): Kentaro Hayashi*+ and Ke-Hai Yuan
Companies: Georgia State University and University of Notre Dame
Address: MSC 6A0445, Dept. of Edu. Policy Studies, Atlanta, GA, 30303-3086,
Keywords: skewness ; kurtosis ; structural equation modeling ; robust transformation ; factor loadings
Abstract:

Bayesian factor analysis assumes the normal distribution of the current sample conditional on the parameters. Practical data in social and behavioral sciences typically have significant skewness and kurtosis. If the normality assumption is not attainable, the posterior analysis will be inacurate although the Bayes factor analysis depends less on the current data due to prior information. We propose to apply a robust procedure to the sample before performing a Bayesian factor analysis. Examples show that this procedure leads to a more accurate evaluation of the factor structure when data contain outliers.

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