Censored mean variance sure independence screening for ultrahigh dimensional survival data

Wei Zhong, Jiping Wang and Xiaolin Chen

Computational Statistics & Data Analysis, 2021, vol. 159, issue C

Abstract: Feature screening has become an indispensable statistical modeling tool for ultrahigh dimensional data analysis. This article introduces a new model-free marginal feature screening approach for ultrahigh dimensional survival data with right censoring. The new procedure could be used for survival data with both ultrahigh dimensional categorical and continuous covariates. Motivated by Cui et al. (2015), a censored mean variance index (cMV) is proposed to measure the dependence between a survival outcome and a categorical covariate. Then a slice-and-fuse method is exploited to modify the cMV index adaptive to continuous covariates. The sure independence screening based on the censored mean variance index (cMV-SIS) is proposed to identify the important covariates for ultrahigh dimensional data with censored survival outcomes. It enjoys many appealing merits inherited in the mean variance index. It is model-free and thus robust to model misspecification. It is also robust to heavy tails and outliers in covariates. Moreover, the sure screening properties are theoretically investigated for both categorical and continuous covariates under some mild technical conditions. Extensive numerical simulations and a real data example have demonstrated the competitive performances of the proposed feature screening method.

Keywords: Feature screening; Mean variance index; Right censoring; Sure screening property; Survival data; Ultrahigh dimensionality (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947321000402
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:159:y:2021:i:c:s0167947321000402

DOI: 10.1016/j.csda.2021.107206

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().