File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
 Publisher Website: 10.1111/j.15410420.2006.00575.x
 Scopus: eid_2s2.033845494068
 PMID: 17156273
 WOS: WOS:000242771800005
 Find via
Supplementary

Bookmarks:
 CiteULike: 1
 Citations:
 Appears in Collections:
Article: Semiparametric analysis of zeroinflated count data
Title  Semiparametric analysis of zeroinflated count data 

Authors  
Keywords  Asymptotically efficient Generalized partly linear model Sieve maximum likelihood estimator Zeroinflated Poisson regression model 
Issue Date  2006 
Publisher  Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM 
Citation  Biometrics, 2006, v. 62 n. 4, p. 9961003+1283 How to Cite? 
Abstract  Medical 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 zeroinflated Poisson regression model, which hypothesizes a twopoint heterogeneity in the population characterized by a binary random effect, is generally used to model such data. Subjects are broadly categorized into the lowrisk group leading to structural zero counts and highrisk (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 lowrisk group by means of a logistic regression formulation; and (ii) the magnitude of the counts, given that the subject is from the highrisk 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 zeroinflated 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 4week period. © 2006, The International Biometric Society. 
Persistent Identifier  http://hdl.handle.net/10722/82835 
ISSN  2017 Impact Factor: 1.524 2015 SCImago Journal Rankings: 1.906 
ISI Accession Number ID  
References 
DC Field  Value  Language 

dc.contributor.author  Lam, KF  en_HK 
dc.contributor.author  Xue, H  en_HK 
dc.contributor.author  Bun Cheung, Y  en_HK 
dc.date.accessioned  20100906T08:33:57Z   
dc.date.available  20100906T08:33:57Z   
dc.date.issued  2006  en_HK 
dc.identifier.citation  Biometrics, 2006, v. 62 n. 4, p. 9961003+1283  en_HK 
dc.identifier.issn  0006341X  en_HK 
dc.identifier.uri  http://hdl.handle.net/10722/82835   
dc.description.abstract  Medical 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 zeroinflated Poisson regression model, which hypothesizes a twopoint heterogeneity in the population characterized by a binary random effect, is generally used to model such data. Subjects are broadly categorized into the lowrisk group leading to structural zero counts and highrisk (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 lowrisk group by means of a logistic regression formulation; and (ii) the magnitude of the counts, given that the subject is from the highrisk 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 zeroinflated 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 4week period. © 2006, The International Biometric Society.  en_HK 
dc.language  eng  en_HK 
dc.publisher  Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM  en_HK 
dc.relation.ispartof  Biometrics  en_HK 
dc.rights  Biometrics. Copyright © Blackwell Publishing Ltd.  en_HK 
dc.subject  Asymptotically efficient  en_HK 
dc.subject  Generalized partly linear model  en_HK 
dc.subject  Sieve maximum likelihood estimator  en_HK 
dc.subject  Zeroinflated Poisson regression model  en_HK 
dc.title  Semiparametric analysis of zeroinflated count data  en_HK 
dc.type  Article  en_HK 
dc.identifier.openurl  http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006341X&volume=62&spage=996&epage=1003&date=2006&atitle=Semiparametric+analysis+of+zeroinflated+count+data  en_HK 
dc.identifier.email  Lam, KF: hrntlkf@hkucc.hku.hk  en_HK 
dc.identifier.authority  Lam, KF=rp00718  en_HK 
dc.description.nature  link_to_subscribed_fulltext   
dc.identifier.doi  10.1111/j.15410420.2006.00575.x  en_HK 
dc.identifier.pmid  17156273   
dc.identifier.scopus  eid_2s2.033845494068  en_HK 
dc.identifier.hkuros  129209  en_HK 
dc.relation.references  http://www.scopus.com/mlt/select.url?eid=2s2.033845494068&selection=ref&src=s&origin=recordpage  en_HK 
dc.identifier.volume  62  en_HK 
dc.identifier.issue  4  en_HK 
dc.identifier.spage  996  en_HK 
dc.identifier.epage  1003+1283  en_HK 
dc.identifier.eissn  15410420   
dc.identifier.isi  WOS:000242771800005   
dc.publisher.place  United Kingdom  en_HK 
dc.identifier.scopusauthorid  Lam, KF=8948421200  en_HK 
dc.identifier.scopusauthorid  Xue, H=7202517221  en_HK 
dc.identifier.scopusauthorid  Bun Cheung, Y=6504435141  en_HK 
dc.identifier.citeulike  968516   