An alternative REML estimation of covariance matrices in linear mixed models

Erning Li; Mohsen Pourahmadi

doi:10.1016/j.spl.2012.12.028

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An alternative REML estimation of covariance matrices in linear mixed models

Journal article

Peer reviewed

An alternative REML estimation of covariance matrices in linear mixed models

Erning Li and Mohsen Pourahmadi

Statistics & probability letters, Vol.83(4), pp.1071-1077

04/2013

DOI: 10.1016/j.spl.2012.12.028

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Abstract

We propose a data-driven procedure for modeling covariance matrices in linear mixed-effects models with minimal distributional assumption on the random effects. It is based on elimination of the random effects using a transformation of the response variable. The approach makes it possible for the first time to disentangle the covariance matrices and model them separately. The performance of the proposed method is assessed via simulations and real data.

Cholesky decomposition

Covariance matrices

Longitudinal data

Mixed models

Restricted or residual maximum likelihood (REML)

Details

Title: Subtitle: An alternative REML estimation of covariance matrices in linear mixed models
Creators: Erning Li - University of Iowa
Mohsen Pourahmadi - Texas A&M University
Resource Type: Journal article
Publication Details: Statistics & probability letters, Vol.83(4), pp.1071-1077
Publisher: Elsevier B.V
DOI: 10.1016/j.spl.2012.12.028
ISSN: 0167-7152
eISSN: 1879-2103
Language: English
Date published: 04/2013
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257615202771

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