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High‐dimensional single‐index models with censored responses
Author(s) -
Huang Hailin,
Shangguan Jizi,
Li Xinmin,
Liang Hua
Publication year - 2020
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.8571
Subject(s) - univariate , estimator , nonparametric statistics , index (typography) , computer science , statistics , sample (material) , single index model , function (biology) , censoring (clinical trials) , multivariate statistics , mathematics , econometrics , chemistry , chromatography , evolutionary biology , world wide web , biology
In this article, we study the estimation of high‐dimensional single index models when the response variable is censored. We hybrid the estimation methods for high‐dimensional single‐index models (but without censorship) and univariate nonparametric models with randomly censored responses to estimate the index parameters and the link function and apply the proposed methods to analyze a genomic dataset from a study of diffuse large B‐cell lymphoma. We evaluate the finite sample performance of the proposed procedures via simulation studies and establish large sample theories for the proposed estimators of the index parameter and the nonparametric link function under certain regularity conditions.