Ultrahigh dimensional feature screening via projection

Xingxiang Li, Guosheng Cheng, Liming Wang, Peng Lai and Fengli Song

Computational Statistics & Data Analysis, 2017, vol. 114, issue C, 88-104

Abstract: This work is concerned with feature screening for linear model with multivariate responses and ultrahigh dimensional covariates. Instead of utilizing the correlation between every response and covariate, the linear space spanned by the multivariate responses is considered in this paper. Based on the projection theory, each covariate is projected on the linear space spanned by the multivariate responses, and a new screening procedure called projection screening (PS) is proposed. The sure screening and ranking consistency properties are established under some regular conditions. To solve some difficulties in marginally feature screening for linear model and enhance the screening performance of the proposed procedure, an iterative projection screening (IPS) procedure is constructed. The finite sample properties of the proposed procedure are assessed by Monte Carlo simulation studies and a real-life data example is analysed.

Keywords: Feature screening; Projection theory; Sure screening property; Ranking consistency property (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947317300798
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:114:y:2017:i:c:p:88-104

DOI: 10.1016/j.csda.2017.04.006

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 ().