Goodness-of-fit tests in mixed models

Gerda Claeskens, Jeffrey D. Hart

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of interest. Our tests are nonparametric in the sense that they are designed to detect virtually any alternative to normality. In case of rejection of the null hypothesis, the nonparametric estimation method that is used to construct a test provides an estimator of the alternative distribution. © 2009 Sociedad de Estadística e Investigación Operativa.
Original languageEnglish (US)
Pages (from-to)213-239
Number of pages27
JournalTest
Volume18
Issue number2
DOIs
StatePublished - May 12 2009
Externally publishedYes

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