Improved two-stage model averaging for high-dimensional linear regression, with application to Riboflavin data analysis.

ABSTRACT:

Background

Model averaging has attracted increasing attention in recent years for the analysis of high-dimensional data. By weighting several competing statistical models suitably, model averaging attempts to achieve stable and improved prediction. In this paper, we develop a two-stage model averaging procedure to enhance accuracy and stability in prediction for high-dimensional linear regression. First we employ a high-dimensional variable selection method such as LASSO to screen redundant predictors and construct a class of candidate models, then we apply the jackknife cross-validation to optimize model weights for averaging.

Results

In simulation studies, the proposed technique outperforms commonly used alternative methods under high-dimensional regression setting, in ter

SUBMITTER: Pan J 

PROVIDER: S-EPMC7992957 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature