High dimensional censored quantile regression
type: Item
instance of: scholarly article
title: High dimensional censored quantile regression
zbMATH Open document ID: 1416.62236
DOI: 10.1214/17-AOS1551
author: Qi Zheng
author: Limin Peng
author: Xuming He
published in: The Annals of Statistics
publication date: 2018-04-27T00:00:00Z
review text: For the logarithm of a survival time which may be censored, inference of $p$-dimensional unknown coefficients in a linear quantile regression model for its conditional quantile function is considered in this paper. When the number of variables in the model is much larger than the sample size, procedures based on Lasso type penalties and their weighted versions are proposed to simultaneously select the variables which have influences on the target segment of the distribution of the log-survival time and estimate the regression coefficients for the selected variables. Theoretical properties of the proposed procedures are derived, simulation studies confirmed theoretical findings, and an application to real data illustrates the practical utilities of the proposed procedures.
reviewed by: Dongsheng Tu
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62N01
Mathematics Subject Classification ID: 62N02
Mathematics Subject Classification ID: 62J07
zbMATH DE Number: 6865113
zbMATH Keywords: survival data
zbMATH Keywords: penalty functions
zbMATH Keywords: censored quantile regression
zbMATH Keywords: high dimension
describes a project that uses: quantreg
MaRDI profile type: Publication
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Wikidata QID: Q57826138
description: scientific article; zbMATH DE number 6865113