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August 2011 Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter
Charles E. McCulloch, John M. Neuhaus
Statist. Sci. 26(3): 388-402 (August 2011). DOI: 10.1214/11-STS361


Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitudinal data, we examine the impact of random effects distribution misspecification on a variety of inferences, including prediction, inference about covariate effects, prediction of random effects and estimation of random effects variances. We describe examples, theoretical calculations and simulations to elucidate situations in which the specification is and is not important. A key conclusion is the large degree of robustness of maximum likelihood for a wide variety of commonly encountered situations.


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Charles E. McCulloch. John M. Neuhaus. "Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter." Statist. Sci. 26 (3) 388 - 402, August 2011.


Published: August 2011
First available in Project Euclid: 31 October 2011

zbMATH: 1246.62169
MathSciNet: MR2917962
Digital Object Identifier: 10.1214/11-STS361

Keywords: maximum likelihood , mixed models , parametric modeling

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.26 • No. 3 • August 2011
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