Handle URI:
http://hdl.handle.net/10754/598423
Title:
Goodness-of-fit tests in mixed models
Authors:
Claeskens, Gerda; Hart, Jeffrey D.
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.
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.
Publisher:
Springer Nature
Journal:
TEST
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
12-May-2009
DOI:
10.1007/s11749-009-0148-8
Type:
Article
ISSN:
1133-0686; 1863-8260
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.
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Full metadata record

DC FieldValue Language
dc.contributor.authorClaeskens, Gerdaen
dc.contributor.authorHart, Jeffrey D.en
dc.date.accessioned2016-02-25T13:20:28Zen
dc.date.available2016-02-25T13:20:28Zen
dc.date.issued2009-05-12en
dc.identifier.citationClaeskens 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.en
dc.identifier.issn1133-0686en
dc.identifier.issn1863-8260en
dc.identifier.doi10.1007/s11749-009-0148-8en
dc.identifier.urihttp://hdl.handle.net/10754/598423en
dc.description.abstractMixed 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.en
dc.description.sponsorshipPart 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.en
dc.publisherSpringer Natureen
dc.subjectHypothesis testen
dc.subjectMinimum distanceen
dc.subjectMixed modelen
dc.subjectNonparametric testen
dc.subjectOrder selectionen
dc.titleGoodness-of-fit tests in mixed modelsen
dc.typeArticleen
dc.identifier.journalTESTen
dc.contributor.institutionKU Leuven, 3000 Leuven, Belgiumen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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