Robust linear mixed models using the skew t distribution with application to schizophrenia data

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 lo...

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Veröffentlicht in:Biometrical journal 2010-08, Vol.52 (4), p.449-469
Hauptverfasser: Ho, Hsiu J., Lin, Tsung-I.
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Lin, Tsung-I.
description 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.
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source MEDLINE; Wiley Online Library Journals Frontfile Complete
subjects AECM algorithm
Algorithms
Applications
Biology, psychology, social sciences
Biometrics
Data processing
Distribution theory
Exact sciences and technology
General topics
Humans
Intermittent missing values
Likelihood Functions
Linear Models
Mathematics
Mental disorders
Multivariate Analysis
Outliers
Probability and statistics
Random effects
Randomized Controlled Trials as Topic
Schizophrenia
Schizophrenia - drug therapy
Sciences and techniques of general use
Skew t linear mixed model
Statistics
Tails
title Robust linear mixed models using the skew t distribution with application to schizophrenia data
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