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Article: Semiparametric analysis of zero-inflated count data

TitleSemiparametric analysis of zero-inflated count data
Authors
KeywordsAsymptotically efficient
Generalized partly linear model
Sieve maximum likelihood estimator
Zero-inflated Poisson regression model
Issue Date2006
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2006, v. 62 n. 4, p. 996-1003+1283 How to Cite?
AbstractMedical and public health research often involve the analysis of count data that exhibit a substantially large proportion of zeros, such as the number of heart attacks and the number of days of missed primary activities in a given period. A zero-inflated Poisson regression model, which hypothesizes a two-point heterogeneity in the population characterized by a binary random effect, is generally used to model such data. Subjects are broadly categorized into the low-risk group leading to structural zero counts and high-risk (or normal) group so that the counts can be modeled by a Poisson regression model. The main aim is to identify the explanatory variables that have significant effects on (i) the probability that the subject is from the low-risk group by means of a logistic regression formulation; and (ii) the magnitude of the counts, given that the subject is from the high-risk group by means of a Poisson regression where the effects of the covariates are assumed to be linearly related to the natural logarithm of the mean of the counts. In this article we consider a semiparametric zero-inflated Poisson regression model that postulates a possibly nonlinear relationship between the natural logarithm of the mean of the counts and a particular covariate. A sieve maximum likelihood estimation method is proposed. Asymptotic properties of the proposed sieve maximum likelihood estimators are discussed. Under some mild conditions, the estimators are shown to be asymptotically efficient and normally distributed. Simulation studies were carried out to investigate the performance of the proposed method. For illustration purpose, the method is applied to a data set from a public health survey conducted in Indonesia where the variable of interest is the number of days of missed primary activities due to illness in a 4-week period. © 2006, The International Biometric Society.
Persistent Identifierhttp://hdl.handle.net/10722/82835
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorXue, Hen_HK
dc.contributor.authorBun Cheung, Yen_HK
dc.date.accessioned2010-09-06T08:33:57Z-
dc.date.available2010-09-06T08:33:57Z-
dc.date.issued2006en_HK
dc.identifier.citationBiometrics, 2006, v. 62 n. 4, p. 996-1003+1283en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/82835-
dc.description.abstractMedical and public health research often involve the analysis of count data that exhibit a substantially large proportion of zeros, such as the number of heart attacks and the number of days of missed primary activities in a given period. A zero-inflated Poisson regression model, which hypothesizes a two-point heterogeneity in the population characterized by a binary random effect, is generally used to model such data. Subjects are broadly categorized into the low-risk group leading to structural zero counts and high-risk (or normal) group so that the counts can be modeled by a Poisson regression model. The main aim is to identify the explanatory variables that have significant effects on (i) the probability that the subject is from the low-risk group by means of a logistic regression formulation; and (ii) the magnitude of the counts, given that the subject is from the high-risk group by means of a Poisson regression where the effects of the covariates are assumed to be linearly related to the natural logarithm of the mean of the counts. In this article we consider a semiparametric zero-inflated Poisson regression model that postulates a possibly nonlinear relationship between the natural logarithm of the mean of the counts and a particular covariate. A sieve maximum likelihood estimation method is proposed. Asymptotic properties of the proposed sieve maximum likelihood estimators are discussed. Under some mild conditions, the estimators are shown to be asymptotically efficient and normally distributed. Simulation studies were carried out to investigate the performance of the proposed method. For illustration purpose, the method is applied to a data set from a public health survey conducted in Indonesia where the variable of interest is the number of days of missed primary activities due to illness in a 4-week period. © 2006, The International Biometric Society.en_HK
dc.languageengen_HK
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.rightsBiometrics. Copyright © Blackwell Publishing Ltd.en_HK
dc.subjectAsymptotically efficienten_HK
dc.subjectGeneralized partly linear modelen_HK
dc.subjectSieve maximum likelihood estimatoren_HK
dc.subjectZero-inflated Poisson regression modelen_HK
dc.titleSemiparametric analysis of zero-inflated count dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-341X&volume=62&spage=996&epage=1003&date=2006&atitle=Semiparametric+analysis+of+zero-inflated+count+dataen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1541-0420.2006.00575.xen_HK
dc.identifier.pmid17156273-
dc.identifier.scopuseid_2-s2.0-33845494068en_HK
dc.identifier.hkuros129209en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33845494068&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume62en_HK
dc.identifier.issue4en_HK
dc.identifier.spage996en_HK
dc.identifier.epage1003+1283en_HK
dc.identifier.eissn1541-0420-
dc.identifier.isiWOS:000242771800005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridXue, H=7202517221en_HK
dc.identifier.scopusauthoridBun Cheung, Y=6504435141en_HK
dc.identifier.citeulike968516-

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