Weak signals in high‐dimensional regression: Detection, estimation and prediction. (25th May 2018)

• Record Type: Journal Article
• Authors: Li, Yanming; Hong, Hyokyoung G.; Ahmed, S. Ejaz; Li, Yi
• Is Part Of: Applied stochastic models in business and industry. Volume 35:Number 2(2019)
• Page Start: 283
• Page End: 298
• Publication Date: 2018-05-25
• Subjects: high‐dimensional data -- Lasso -- post‐selection shrinkage estimation -- variable selection -- weak signal detection; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2340
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract: Regularization methods, including Lasso, group Lasso, and SCAD, typically focus on selecting variables with strong effects while ignoring weak signals. This may result in biased prediction, especially when weak signals outnumber strong signals. This paper aims to incorporate weak signals in variable selection, estimation, and prediction. We propose a two‐stage procedure, consisting of variable selection and postselection estimation. The variable selection stage involves a covariance‐insured screening for detecting weak signals, whereas the postselection estimation stage involves a shrinkage estimator for jointly estimating strong and weak signals selected from the first stage. We term the proposed method as the covariance‐insured screening‐based postselection shrinkage estimator. We establish asymptotic properties for the proposed method and show, via simulations, that incorporating weak signals can improve estimation and prediction performance. We apply the proposed method to predict the annual gross domestic product rates based on various socioeconomic indicators for 82 countries.