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Article: Influence diagnostics for two-component Poisson mixture regression models: Applications in public health

TitleInfluence diagnostics for two-component Poisson mixture regression models: Applications in public health
Authors
KeywordsCount data
Diagnostics
Heterogeneity
Local influence
Poisson mixture regression
Random effects
Issue Date2005
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2005, v. 24 n. 19, p. 3053-3071 How to Cite?
AbstractIn many medical and health applications, Poisson mixture regression models are commonly used to analyse heterogeneous count data. Motivated by two data sets drawn from public health studies, influence diagnostics are proposed for assessing the sensitivity of the fitted two-component Poisson mixture regression models. Under various perturbations of the observed data or model assumptions, influence assessments based on the local influence approach are developed for detecting clusters and/or individual observations that impact on the estimation of model parameters. Results from studies on recurrent urinary tract infections and maternity length of stay illustrate the usefulness of the influence diagnostics. Copyright © 2005 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/82868
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorXiang, Len_HK
dc.contributor.authorYau, KKWen_HK
dc.contributor.authorLee, AHen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:34:19Z-
dc.date.available2010-09-06T08:34:19Z-
dc.date.issued2005en_HK
dc.identifier.citationStatistics In Medicine, 2005, v. 24 n. 19, p. 3053-3071en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82868-
dc.description.abstractIn many medical and health applications, Poisson mixture regression models are commonly used to analyse heterogeneous count data. Motivated by two data sets drawn from public health studies, influence diagnostics are proposed for assessing the sensitivity of the fitted two-component Poisson mixture regression models. Under various perturbations of the observed data or model assumptions, influence assessments based on the local influence approach are developed for detecting clusters and/or individual observations that impact on the estimation of model parameters. Results from studies on recurrent urinary tract infections and maternity length of stay illustrate the usefulness of the influence diagnostics. Copyright © 2005 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectCount dataen_HK
dc.subjectDiagnosticsen_HK
dc.subjectHeterogeneityen_HK
dc.subjectLocal influenceen_HK
dc.subjectPoisson mixture regressionen_HK
dc.subjectRandom effectsen_HK
dc.titleInfluence diagnostics for two-component Poisson mixture regression models: Applications in public healthen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6715&volume=24&issue=19&spage=3053&epage=3071&date=2005&atitle=Influence+diagnostics+for+two-component+Poisson+mixture+regression+models:+applications+in+public+healthen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.2160en_HK
dc.identifier.pmid16149127-
dc.identifier.scopuseid_2-s2.0-26444524022en_HK
dc.identifier.hkuros120290en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26444524022&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue19en_HK
dc.identifier.spage3053en_HK
dc.identifier.epage3071en_HK
dc.identifier.isiWOS:000232215600010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridXiang, L=7102911425en_HK
dc.identifier.scopusauthoridYau, KKW=7101941425en_HK
dc.identifier.scopusauthoridLee, AH=26643271800en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

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