A Bayesian approach with generalized ridge estimation for high-dimensional regression and testing

作者：Yang Szu Peng; Emura Takeshi^*

来源：Communications in Statistics - Simulation and Computation, 2017, 46(8): 6083-6105.

DOI：10.1080/03610918.2016.1193195

• Bayes estimator
• Compound covariate estimator
• Linear model
• Mean square error
• Shrinkage estimator
• Statistical decision theory

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.

• 出版日期2017
• 单位中央民族大学