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 |
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creator | Ho, Hsiu J. 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. |
doi_str_mv | 10.1002/bimj.200900184 |
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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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