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Bayesian Variable Selection in Generalized Linear Mixed Models

  • Bo Cai
  • David B. Dunson
Part of the Lecture Notes in Statistics book series (LNS, volume 192)

Abstract

Repeated measures and longitudinal data are commonly collected for analysis in epidemiology, clinical trials, biology, sociology, and economic sciences. In such studies, a response is measured repeatedly over time for each subject under study, and the number and timing of the observations often varies among subjects. In contrast to cross-sectional studies that collect a single measurement per subject, longitudinal studies have the extra complication of within-subject dependence in the repeated measures. Such dependence can be thought to arise due to the impact of unmeasured predictors. Main effects of unmeasured predictors lead to variation in the average level of response among subjects, while interactions with measured predictors lead to heterogeneity in the regression coefficients. This justification has motivated random effects models, which allow the intercept and slopes in a regression model to be subject-specific. Random effects models are broadly useful for modeling of dependence not only for longitudinal data but also in multicenter studies, meta analysis and functional data analysis.

Keywords

Marginal Likelihood Candidate Predictor Full Conditional Distribution Bayesian Variable Selection Posterior Model Probability 
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 Epidemiology and BiostatisticsArnold School of Public Health, University of South CarolinaColumbiaUSA
  2. 2.Biostatistics BranchNational Institute of Environmental Health Sciences Research Triangle ParkUSA

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