ECM-based maximum likelihood inference for multivariate linear mixed models with autoregressive errors

For the analysis of longitudinal data with multiple characteristics, we are devoted to providing additional tools for multivariate linear mixed models in which the errors are assumed to be serially correlated according to an autoregressive process. We present a computationally flexible ECM procedure...

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Bibliographic Details
Published in:Computational statistics & data analysis Vol. 54; no. 5; pp. 1328 - 1341
Main Authors: Wang, Wan-Lun, Fan, Tsai-Hung
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.05.2010
Elsevier
Series:Computational Statistics & Data Analysis
Subjects:
Exact sciences and technology
General topics
Linear inference, regression
Mathematics
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Sciences and techniques of general use
Statistics
Score test
Error estimation
Prediction theory
Autoregressive model
Multivariate analysis
Stochastic process
Statistical simulation
Linear model
Statistical test
Repeated measurement
Random effect
Score statistics
Data analysis
Test statistic
Prediction
AIDS
Statistical association
Statistical estimation
Infection
Statistical computation
Correlation analysis
Viral disease
Mixed model
Filtering theory
Maximum likelihood
Autocorrelation
Autoregressive processes
ISSN:0167-9473, 1872-7352
Online Access:Get full text
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Description
Summary:For the analysis of longitudinal data with multiple characteristics, we are devoted to providing additional tools for multivariate linear mixed models in which the errors are assumed to be serially correlated according to an autoregressive process. We present a computationally flexible ECM procedure for obtaining the maximum likelihood estimates of model parameters. A score test statistic for testing the existence of autocorrelation among within-subject errors of each characteristic is derived. The techniques for the estimation of random effects and the prediction of further responses given past repeated measures are also investigated. The methodology is illustrated through an application to a set of AIDS data and two small simulation studies.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2009.11.021