Modeling the random effects covariance matrix for the generalized linear mixed models

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 28 September 2011 Keunbaik Lee, JungBok Lee, Joseph Hagan, Jae Keun Yoo Generalized linear mixed models (GLMM) are commonly used to analyze longitudinal categorical data. In these models, we typically assume that the random effects covariance matrix is constant across 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.

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 28 September 2011 Keunbaik Lee, JungBok Lee, Joseph Hagan, Jae Keun Yoo Generalized linear mixed models (GLMM) are commonly used to analyze longitudinal categorical data. In these models, we typically assume that the random effects covariance matrix is constant across 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.

See the original article here:
Modeling the random effects covariance matrix for the generalized linear mixed models

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