Statistics – Methodology
Scientific paper
2012-01-10
Statistical Science 2011, Vol. 26, No. 3, 388-402
Statistics
Methodology
Published in at http://dx.doi.org/10.1214/11-STS361 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M
Scientific paper
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.
McCulloch Charles E.
Neuhaus John M.
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