A Robust Approach to Linear Mixed Effects Models Based on the Skew t Distribution

• Activity Number: 308
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Tsung-I Lin*+ and Hsiu J. Ho
• Companies: National Chung Hsing University and National Chung Hsing University
• Keywords: AECM algorithm ; intermittent missing values ; outliers ; random effects ; SNLMM ; STLMM

Abstract

We consider an extension of linear mixed models by assuming a multivariate skew t distribution for the random effects and a multivariate t distribution for the error terms. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously among continuous longitudinal data. We present an efficient alternating expectation-conditional maximization (AECM) algorithm for the computation of maximum likelihood estimates of parameters on the basis of two convenient hierarchical formulations. The techniques for the prediction of random effects and intermittent missing values under this model are also investigated. Our methodologies are illustrated through an application to schizophrenia data.