期刊
STATISTICS IN MEDICINE
卷 34, 期 5, 页码 876-886出版社
WILEY-BLACKWELL
DOI: 10.1002/sim.6382
关键词
asymptotic normality; B-spline; generalized partially linear model; single-index model; zero-inflated count data
类别
资金
- National Natural Sciences Foundation of China (NSFC) [11101063, 11471065, 11371077]
- NSFC Tianyuan fund for Mathematics [11326179]
- NSFC [11401391]
- Research Grants Council of Hong Kong [705613]
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. Copyright (C) 2014 John Wiley & Sons, Ltd.
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