Double shrunken selection operator

TL;DR

This paper introduces a Stein-type shrinkage extension of LASSO that enhances feature selection and prediction accuracy, demonstrating improved performance over traditional LASSO in numerical risk assessment and prostate cancer data analysis.

Contribution

It proposes a novel Stein-type LASSO estimator that shrinks coefficients more aggressively, improving risk performance and prediction accuracy over standard LASSO.

Findings

Stein-type LASSO has lower relative MSE than LASSO.

Stein-type LASSO yields better prediction error.

Improved feature selection in prostate cancer data.

Abstract

The least absolute shrinkage and selection operator (LASSO) of Tibshirani (1996) is a prominent estimator which selects significant (under some sense) features and kills insignificant ones. Indeed the LASSO shrinks features lager than a noise level to zero. In this paper, we force LASSO to be shrunken more by proposing a Stein-type shrinkage estimator emanating from the LASSO, namely the Stein-type LASSO. The newly proposed estimator proposes good performance in risk sense numerically. Variants of this estimator have smaller relative MSE and prediction error, compared to the LASSO, in the analysis of prostate cancer data set.