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• W1506661708 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. Copyright © 2014 John Wiley & Sons, Ltd." @default.
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• W1506661708 date "2015-02-28" @default.
• W1506661708 modified "2023-10-03" @default.
• W1506661708 title "Generalized partially linear single-index model for zero-inflated count data" @default.
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• W1506661708 doi "https://doi.org/10.1002/sim.6382" @default.
• W1506661708 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25421596" @default.
• W1506661708 hasPublicationYear "2015" @default.
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