Document Detail


Semiparametric analysis of zero-inflated count data.
MedLine Citation:
PMID:  17156273     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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 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.
Authors:
K F Lam; Hongqi Xue; Yin Bun Cheung
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Biometrics     Volume:  62     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2006 Dec 
Date Detail:
Created Date:  2006-12-12     Completed Date:  2007-03-07     Revised Date:  2014-02-19    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  996-1003     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Biometry / methods*
Data Collection
Data Interpretation, Statistical
Humans
Indonesia
Likelihood Functions
Logistic Models
Models, Statistical
Poisson Distribution
Public Health / statistics & numerical data
Regression Analysis
Grant Support
ID/Acronym/Agency:
G0700837//Medical Research Council

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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