title: Generalized partially linear single-index model for zero-inflated count data.
abstract: Count data often arise in biomedical studies, while there could be a special feature with excessive zeros in the observed counts. The zero-inflated Poisson model provides a natural approach to accounting for the excessive zero counts. In the semiparametric framework, we propose a generalized partially linear single-index model for the mean of the Poisson component, the probability of zero, or both. We develop the estimation and inference procedure via a profile maximum likelihood method. Under some mild conditions, we establish the asymptotic properties of the profile likelihood estimators. The finite sample performance of the proposed method is demonstrated by simulation studies, and the new model is illustrated with a medical care dataset.
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, 116024, China.
authors.forename: Xiaoguang
authors.initials: X
authors.lastname: Wang
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.forename: Liang
authors.initials: L
authors.lastname: Yu
authors.forename: Guosheng
authors.initials: G
authors.lastname: Yin
mesh_terms: D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005102:Health Expenditures / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D018709:Statistics, Nonparametric
keywords: B-spline; asymptotic normality; generalized partially linear model; single-index model; zero-inflated count data