Abstract
Random effects models are widely used in analyzing dependent data, which are collected routinely in a broad variety of application areas. For example, longitudinal studies collect repeated observations for each study subject, while multi-center studies collect data for patients nested within study centers. In such settings, it is natural to suppose that dependence arises due to the impact of important unmeasured predictors that may interact with measured predictors. This viewpoint naturally leads to random effects models in which the regression coefficients vary across the different subjects. In this chapter, we use the term “subject” broadly to refer to the independent experimental units. For example, in longitudinal studies, the subjects are the individuals under study, while in multi-center studies the subjects correspond to the study centers.
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Acknowledgments
This work was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences, and by the National Science Foundation under Agreement No. DMS-0112069 with the Statistical and Mathematical Sciences Institute (SAMSI). The authors are grateful to Merlise Clyde and Abel Rodriguez at Duke University and to the participants of the Model Uncertainty working group of the SAMSI Program on Latent Variable Modeling in the Social Sciences.
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Kinney, S.K., Dunson, D.B. (2008). Bayesian Model Uncertainty in Mixed Effects Models. In: Dunson, D.B. (eds) Random Effect and Latent Variable Model Selection. Lecture Notes in Statistics, vol 192. Springer, New York, NY. https://doi.org/10.1007/978-0-387-76721-5_3
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