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Modeling of random effects covariance matrix in marginalized random effects models
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 Title & Authors
Modeling of random effects covariance matrix in marginalized random effects models
Lee, Keunbaik; Kim, Seolhwa;
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Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.
Autocorrelation;modied Cholesky decomposition;heterogeneity;Quasi-Monte Carlo;
 Cited by
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