Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Modeling of random effects covariance matrix in marginalized random effects models

  • Lee, Keunbaik (Department of Statistics, Sungkyunkwan University) ;
  • Kim, Seolhwa (Department of Statistics, Sungkyunkwan University)
  • Received : 2016.02.12
  • Accepted : 2016.04.12
  • Published : 2016.05.31
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Abstract

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.

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