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http://dx.doi.org/10.5351/KJAS.2015.28.2.211

Survey of Models for Random Effects Covariance Matrix in Generalized Linear Mixed Model  

Kim, Jiyeong (Department of Statistics, Sungkyunkwan University)
Lee, Keunbaik (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 211-219 More about this Journal
Abstract
Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.
Keywords
Longitudinal data; categorical data; modified Cholesky decomposition; moving average Cholesky decomposition; partial autocorrelation matrix;
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Times Cited By KSCI : 1  (Citation Analysis)
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