A new nonparametric screening method for ultrahigh-dimensional survival data

Yanyan Liu, Jing Zhang and Xingqiu Zhao

Computational Statistics & Data Analysis, 2018, vol. 119, issue C, 74-85

Abstract: For ultrahigh-dimensional data, sure independent screening methods can effectively reduce the dimensionality while ensuring that all the active variables can be retained with high probability. However, most existing screening procedures are developed for ultrahigh-dimensional complete data and cannot be applicable to censored survival data. To address the new challenges from censoring, a novel model-free screening method was proposed through the Kolmogorov–Smirnov test statistic that is specially tailored to the ultrahigh-dimensional survival data. The sure screening property was established under some mild regularity conditions, and its superior performance over existing screening methods is demonstrated by our extensive simulation studies. A real data example of gene expression is used to illustrate the application of the proposed fully nonparametric screening procedure.

Keywords: Model-free; Sure screening property; Ultrahigh-dimensional survival data; Variable screening (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947317302219
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:119:y:2018:i:c:p:74-85

DOI: 10.1016/j.csda.2017.10.003

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 ().