Principal Components Adjusted Variable Screening.

TitlePrincipal Components Adjusted Variable Screening.
Publication TypeJournal Article
Year of Publication2017
AuthorsLiu, Zhongkai, Rui Song, Donglin Zeng, and Jiajia Zhang
JournalComput Stat Data Anal
Date Published2017 Jun

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.

Alternate JournalComput Stat Data Anal
Original PublicationPrincipal components adjusted variable screening.
PubMed ID28603325
PubMed Central IDPMC5461980
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States