JSM 2004 - Toronto

Abstract #301051

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Activity Number: 376
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #301051
Title: A Robust Approach to Longitudinal Data Analysis Using the T Linear Mixed Model with AR(1) Serial Correlation
Author(s): Tsung I. Lin*+
Companies: Tunghai University
Address: Dept. of Statistics, Box 823, Taichung, 40704, Taiwan
Keywords: ECME algorithm ; Fisher Information matrix ; longitudinal data ; maximum likelihood estimation ; random effects ; score test
Abstract:

We discuss a robust extension of linear mixed model based on multivariate t distributions. Since longitudinal data are collected over time and hence tend to be serially correlated, we employ a parsimonious first-order autoregressive [AR(1)] dependence structure for the within-subject errors. For parameter estimation, we present a hybrid ECME/scoring algorithm that combines the stability and rapid convergence with standard errors as a by-product. Moreover, we offer a score test statistic for testing the existence of autocorrelation in the within-subject errors. The techniques for estimating random effects and predicting future responses of a subject are also investigated. Numerical results are illustrated with a real clinical trial data.

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