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Article: Testing multivariate normality in incomplete data of small sample size

TitleTesting multivariate normality in incomplete data of small sample size
Authors
KeywordsBayesian Analysis
Em Algorithm
Ibf Sampling
Multiple Imputation
Multivariate Normality Test
Projection Test
Issue Date2005
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal Of Multivariate Analysis, 2005, v. 93 n. 1, p. 164-179 How to Cite?
AbstractIn longitudinal studies with small samples and incomplete data, multivariate normal-based models continue to be a powerful tool for analysis. This has included a broad scope of biomedical studies. Testing the assumption of multivariate normality (MVN) is critical. Although many methods are available for testing normality in complete data with large samples, a few deal with the testing in small samples. For example, Liang et al. (J. Statist. Planning and Inference 86 (2000) 129) propose a projection procedure for testing MVN for complete-data with small samples where the sample sizes may be close to the dimension. To our knowledge, no statistical methods for testing MVN in incomplete data with small samples are yet available. This article develops a test procedure in such a setting using multiple imputations and the projection test. To utilize the incomplete data structure in multiple imputation, we adopt a noniterative inverse Bayes formulae (IBF) sampling procedure instead of the iterative Gibbs sampling to generate iid samples. Simulations are performed for both complete and incomplete data when the sample size is less than the dimension. The method is illustrated with a real study on an anticancer drug. © 2004 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/172466
ISSN
2015 Impact Factor: 0.857
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTan, Men_US
dc.contributor.authorFang, HBen_US
dc.contributor.authorTian, GLen_US
dc.contributor.authorWei, Gen_US
dc.date.accessioned2012-10-30T06:22:40Z-
dc.date.available2012-10-30T06:22:40Z-
dc.date.issued2005en_US
dc.identifier.citationJournal Of Multivariate Analysis, 2005, v. 93 n. 1, p. 164-179en_US
dc.identifier.issn0047-259Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/172466-
dc.description.abstractIn longitudinal studies with small samples and incomplete data, multivariate normal-based models continue to be a powerful tool for analysis. This has included a broad scope of biomedical studies. Testing the assumption of multivariate normality (MVN) is critical. Although many methods are available for testing normality in complete data with large samples, a few deal with the testing in small samples. For example, Liang et al. (J. Statist. Planning and Inference 86 (2000) 129) propose a projection procedure for testing MVN for complete-data with small samples where the sample sizes may be close to the dimension. To our knowledge, no statistical methods for testing MVN in incomplete data with small samples are yet available. This article develops a test procedure in such a setting using multiple imputations and the projection test. To utilize the incomplete data structure in multiple imputation, we adopt a noniterative inverse Bayes formulae (IBF) sampling procedure instead of the iterative Gibbs sampling to generate iid samples. Simulations are performed for both complete and incomplete data when the sample size is less than the dimension. The method is illustrated with a real study on an anticancer drug. © 2004 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmvaen_US
dc.relation.ispartofJournal of Multivariate Analysisen_US
dc.subjectBayesian Analysisen_US
dc.subjectEm Algorithmen_US
dc.subjectIbf Samplingen_US
dc.subjectMultiple Imputationen_US
dc.subjectMultivariate Normality Testen_US
dc.subjectProjection Testen_US
dc.titleTesting multivariate normality in incomplete data of small sample sizeen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.jmva.2004.02.014en_US
dc.identifier.scopuseid_2-s2.0-7044263314en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-7044263314&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume93en_US
dc.identifier.issue1en_US
dc.identifier.spage164en_US
dc.identifier.epage179en_US
dc.identifier.isiWOS:000226176000009-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridTan, M=7401464681en_US
dc.identifier.scopusauthoridFang, HB=7402543028en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.scopusauthoridWei, G=55065533000en_US

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