Statistical Science

Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter

Charles E. McCulloch and John M. Neuhaus

Full-text: Open access

Abstract

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.

Article information

Source
Statist. Sci. Volume 26, Number 3 (2011), 388-402.

Dates
First available in Project Euclid: 31 October 2011

Permanent link to this document
http://projecteuclid.org/euclid.ss/1320066927

Digital Object Identifier
doi:10.1214/11-STS361

Mathematical Reviews number (MathSciNet)
MR2917962

Zentralblatt MATH identifier
1246.62169

Keywords
Maximum likelihood mixed models parametric modeling

Citation

McCulloch, Charles E.; Neuhaus, John M. Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter. Statist. Sci. 26 (2011), no. 3, 388--402. doi:10.1214/11-STS361. http://projecteuclid.org/euclid.ss/1320066927.


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