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Article: Bayesian influence assessment in the growth curve model with unstructured covariance

TitleBayesian influence assessment in the growth curve model with unstructured covariance
Authors
KeywordsBayesian analysis
Case-deletion method
Growth curve model
Kullback-Leibler divergence
Statistical diagnostics
Issue Date2000
PublisherSpringer Verlag.
Citation
Annals Of The Institute Of Statistical Mathematics, 2000, v. 52 n. 4, p. 737-752 How to Cite?
AbstractFrom a Bayesian point of view, in this paper we discuss the influence of a subset of observations on the posterior distributions of parameters in a growth curve model with unstructured covariance. The measure used to assess the influence is based on a Bayesian entropy, namely Kullback-Leibler divergence (KLD). Several new properties of the Bayesian entropy are studied, and analytically closed forms of the KLD measurement both for the matrix-variate normal distribution and the Wishart distribution are established. In the growth curve model, the KLD measurements for all combinations of the parameters are also studied. For illustration, a practical data set is analyzed using the proposed approach, which shows that the diagnostics measurements are useful in practice.
Persistent Identifierhttp://hdl.handle.net/10722/82909
ISSN
2015 Impact Factor: 0.768
2015 SCImago Journal Rankings: 0.931
References

 

DC FieldValueLanguage
dc.contributor.authorPan, JXen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:34:47Z-
dc.date.available2010-09-06T08:34:47Z-
dc.date.issued2000en_HK
dc.identifier.citationAnnals Of The Institute Of Statistical Mathematics, 2000, v. 52 n. 4, p. 737-752en_HK
dc.identifier.issn0020-3157en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82909-
dc.description.abstractFrom a Bayesian point of view, in this paper we discuss the influence of a subset of observations on the posterior distributions of parameters in a growth curve model with unstructured covariance. The measure used to assess the influence is based on a Bayesian entropy, namely Kullback-Leibler divergence (KLD). Several new properties of the Bayesian entropy are studied, and analytically closed forms of the KLD measurement both for the matrix-variate normal distribution and the Wishart distribution are established. In the growth curve model, the KLD measurements for all combinations of the parameters are also studied. For illustration, a practical data set is analyzed using the proposed approach, which shows that the diagnostics measurements are useful in practice.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag.en_HK
dc.relation.ispartofAnnals of the Institute of Statistical Mathematicsen_HK
dc.subjectBayesian analysisen_HK
dc.subjectCase-deletion methoden_HK
dc.subjectGrowth curve modelen_HK
dc.subjectKullback-Leibler divergenceen_HK
dc.subjectStatistical diagnosticsen_HK
dc.titleBayesian influence assessment in the growth curve model with unstructured covarianceen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-3157&volume=52&spage=737&epage=752&date=2000&atitle=Bayesian+influence+assessment+in+the+growth+curve+model+with+unstructured+covarianceen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1023/A:1017581411504-
dc.identifier.scopuseid_2-s2.0-6744236282en_HK
dc.identifier.hkuros57397en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-6744236282&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume52en_HK
dc.identifier.issue4en_HK
dc.identifier.spage737en_HK
dc.identifier.epage752en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridPan, JX=7404098188en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

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