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Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics

  • Daowen Zhang
  • Xihong Lin
Part of the Lecture Notes in Statistics book series (LNS, volume 192)

Abstract

Linear mixed models (Laird and Ware, 1982) and generalized linear mixed models (GLMMs) (Breslow and Clayton, 1993) have been widely used in many research areas, especially in the area of biomedical research, to analyze longitudinal and clustered data and multiple outcome data. In a mixed effects model, subject-specific random effects are used to explicitly model between-subject variation in the data and often assumed to follow a mean zero parametric distribution, e.g., multivariate normal, that depends on some unknown variance components. A large literature was developed in the last two decades for the estimation of regression coefficients and variance components in mixed effects models. See Diggle et al. (2002) and Verbeke and Molenberghs (2000, 2005) for an overview.

Keywords

Score Test Variance Component Likelihood Ratio Test Generalize Linear Mixed Model Smoothing Spline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  2. 2.Department of BiostatisticsHarvard School of Public HealthBostonUSA

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