08. Performance comparisons of power penalized regression against forward stepwise, lasso, and relaxed lasso

Best subset, forward stepwise, and lasso are existing methods for variable selection. The paper Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons'' by Hastie, Tibshirani, and Tibshrani (\textit{Statist. Sci.} \textbf{35(4)}, 579-592, November 2020) presents extensive simulation studies to compare these methods. The paper concludes that the relaxed lasso is the overall winner. Recently, Griffin (2023) proposed improved pathwise coordinate descent algorithms for power penalty regression, which generalized the $\ell_q$ penalty with $0

Keywords

lasso

penalized regression

variable selection 

Presenting Author

Ning Duan, University of Massachusetts Amherst

First Author

Ning Duan, University of Massachusetts Amherst

CoAuthor(s)

Maryclare Griffin, University of Massachusetts Amherst
QIAN ZHAO, University of Massachusetts

Target Audience

Expert

Tracks

Knowledge

Women in Statistics and Data Science 2025