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Semiparametric probit model for informative current status data
Author(s) -
Du Mingyue,
Hu Tao,
Sun Jianguo
Publication year - 2019
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.8106
Subject(s) - probit model , probit , semiparametric regression , econometrics , inference , computer science , statistical inference , semiparametric model , sample (material) , statistics , mathematics , artificial intelligence , nonparametric statistics , chemistry , chromatography
Semiparametric probit models have recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its special features. In this paper, we discuss the fitting of such models to informative current status data, which often occur in many areas such as medical studies and whose analysis has also recently attracted a lot of attention. For inference, a sieve maximum likelihood approach is developed and the methodology is further generalized to a class of generalized semiparametric probit models. A simulation study is conducted to assess the finite sample properties of the presented approach and indicates that it works well in practical situations. An application that motivated this study is provided.