Principal components adjusted variable screening

Zhongkai Liu, Rui Song, Donglin Zeng and Jiajia Zhang

Computational Statistics & Data Analysis, 2017, vol. 110, issue C, 134-144

Abstract: Marginal screening has been established as a fast and effective method for high dimensional variable selection method. There are some drawbacks associated with marginal screening, since the marginal model can be viewed as a model misspecification from the joint true model. A principal components adjusted variable screening method is proposed, which uses top principal components as surrogate covariates to account for the variability of the omitted predictors in generalized linear models. The proposed method is demonstrated with superior numerical performance compared with the competing methods. The efficiency of the method is also illustrated with the analysis of the Affymetrix genechip rat genome 230 2.0 array data and the European American SNPs data.

Keywords: Generalized linear models; Principal components; Variable selection; Sure screening (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947317300026
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:110:y:2017:i:c:p:134-144

DOI: 10.1016/j.csda.2016.12.015

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 ().