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Semiparametric varying‐coefficient model for interval censored data with a cured proportion
Author(s) -
Shao Fang,
Li Jialiang,
Ma Shuangge,
Lee MeiLing Ting
Publication year - 2013
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.6054
Subject(s) - computer science , parametric statistics , consistency (knowledge bases) , nonparametric statistics , inference , statistics , semiparametric regression , statistical inference , model selection , semiparametric model , regression analysis , asymptotic distribution , econometrics , mathematics , artificial intelligence , estimator
Varying‐coefficient models have claimed an increasing portion of statistical research and are now applied to censored data analysis in medical studies. We incorporate such flexible semiparametric regression tools for interval censored data with a cured proportion. We adopted a two‐part model to describe the overall survival experience for such complicated data. To fit the unknown functional components in the model, we take the local polynomial approach with bandwidth chosen by cross‐validation. We establish consistency and asymptotic distribution of the estimation and propose to use bootstrap for inference. We constructed a BIC‐type model selection method to recommend an appropriate specification of parametric and nonparametric components in the model. We conducted extensive simulations to assess the performance of our methods. An application on a decompression sickness data illustrates our methods. Copyright © 2013 John Wiley & Sons, Ltd.