The reciprocal elastic net

A new penalized likelihood method (reciprocal elastic net) is put forward for regularization and variable selection. Our proposal is based on a new class of reciprocal penalty functions, combining the strengths of the reciprocal LASSO regularization and ridge regression. We formulate the reciprocal...

Full description

Saved in:
Bibliographic Details
Published in:Communication in statistics. Case studies and data analysis 2023-10, Vol.9 (4), p.422-436
Main Authors: Alhamzawi, Rahim, Alhamzawi, Ahmed, Mallick, Himel
Format: Article
Language:English
Subjects:
Bayesian analysis
elastic net
LASSO
Reciprocal LASSO
regularization
variable Selection
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A new penalized likelihood method (reciprocal elastic net) is put forward for regularization and variable selection. Our proposal is based on a new class of reciprocal penalty functions, combining the strengths of the reciprocal LASSO regularization and ridge regression. We formulate the reciprocal elastic net problem as an equivalent reciprocal LASSO problem on augmented data, facilitating a direct utilization of the reciprocal LASSO algorithm to generate the entire reciprocal elastic net solution path. We further present the reciprocal adaptive elastic net, fusing the merits of ridge regression with the adaptively weighted reciprocal LASSO regularization. These methods, illustrated through simulated examples and real data analysis, demonstrate satisfactory performance in various diversified scenarios compared to published methods. Finally, we propose Bayesian methods to solve the reciprocal elastic net and reciprocal adaptive elastic net models using Gibbs samplers.
ISSN:2373-7484
2373-7484
DOI:10.1080/23737484.2023.2278106