Type
ArticleAuthors
Claeskens, GerdaHart, Jeffrey D.
KAUST Grant Number
KUS-C1-016-04Date
2009-05-12Online Publication Date
2009-05-12Print Publication Date
2009-08Permanent link to this record
http://hdl.handle.net/10754/598423
Metadata
Show full item recordAbstract
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.Citation
Claeskens G, Hart JD (2009) Goodness-of-fit tests in mixed models. TEST 18: 213–239. Available: http://dx.doi.org/10.1007/s11749-009-0148-8.Sponsors
Part of this research has been performed while G. Claeskens was visiting the IsaacNewton Institute at Cambridge University, U.K. The work of Professor Hart was partially supported byNSF Grant DMS-0604801 and by Award No. KUS-C1-016-04, made by King Abdullah University ofScience and Technology (KAUST). The authors wish to thank W. Ghidey and M. Davidian for providingsome software. They also thank the reviewers for their constructive remarks.Publisher
Springer NatureJournal
TESTae974a485f413a2113503eed53cd6c53
10.1007/s11749-009-0148-8