Title | Principal Components Adjusted Variable Screening. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Liu, Zhongkai, Rui Song, Donglin Zeng, and Jiajia Zhang |
Journal | Comput Stat Data Anal |
Volume | 110 |
Pagination | 134-144 |
Date Published | 2017 Jun |
ISSN | 0167-9473 |
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. |
DOI | 10.1016/j.csda.2016.12.015 |
Alternate Journal | Comput Stat Data Anal |
Original Publication | Principal components adjusted variable screening. |
PubMed ID | 28603325 |
PubMed Central ID | PMC5461980 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 GM047845 / GM / NIGMS NIH HHS / United States U01 NS082062 / NS / NINDS NIH HHS / United States |