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Modeling the random effects covariance matrix for generalized linear mixed models

Title
Modeling the random effects covariance matrix for generalized linear mixed models
Authors
Lee K.Lee J.Hagan J.Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2012
Journal Title
Computational Statistics and Data Analysis
ISSN
0167-9473JCR Link
Citation
vol. 56, no. 6, pp. 1545 - 1551
Indexed
SCIE; SCOPUS WOS scopus
Abstract
Generalized linear mixed models (GLMMs) are commonly used to analyze longitudinal categorical data. In these models, we typically assume that the random effects covariance matrix is constant across the subject and is restricted because of its high dimensionality and its positive definiteness. However, the covariance matrix may differ by measured covariates in many situations, and ignoring this heterogeneity can result in biased estimates of the fixed effects. In this paper, we propose a heterogenous random effects covariance matrix, which depends on covariates, obtained using the modified Cholesky decomposition. This decomposition results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The parameters have a sensible interpretation. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using our proposed model. © 2011 Elsevier B.V. All rights reserved.
DOI
10.1016/j.csda.2011.09.011
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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