A Bayesian approach with generalized ridge estimation for high-dimensional regression and testing. Issue 8 (14th September 2017)

• Record Type: Journal Article
• Authors: Yang, Szu-Peng; Emura, Takeshi
• Is Part Of: Communications in statistics. Volume 46:Issue 8(2017)
• Page Start: 6083
• Page End: 6105
• Publication Date: 2017-09-14
• Subjects: Bayes estimator -- Compound covariate estimator -- Linear model -- Mean square error -- Shrinkage estimator -- Statistical decision theory; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1193195
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This paper adopts a Bayesian strategy for generalized ridge estimation for high-dimensional regression. We also consider significance testing based on the proposed estimator, which is useful for selecting regressors. Both theoretical and simulation studies show that the proposed estimator can simultaneously outperform the ordinary ridge estimator and the LSE in terms of the mean square error (MSE) criterion. The simulation study also demonstrates the competitive MSE performance of our proposal with the Lasso under sparse models. We demonstrate the method using the lung cancer data involving high-dimensional microarrays.