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Generalized partially linear single‐index model for zero‐inflated count data
Author(s) -
Wang Xiaoguang,
Zhang Jun,
Yu Liang,
Yin Guosheng
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6382
Subject(s) - count data , estimator , poisson distribution , zero inflated model , zero (linguistics) , generalized linear model , mathematics , statistics , quasi likelihood , inference , poisson regression , statistical inference , linear model , computer science , artificial intelligence , medicine , population , linguistics , philosophy , environmental health
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 © 2014 John Wiley & Sons, Ltd.