CR-Lasso: Robust cellwise regularized sparse regression

Cellwise contamination remains a challenging problem for data scientists, particularly in research fields that require the selection of sparse features. Traditional robust methods may not be feasible nor efficient in dealing with such contaminated datasets. A robust Lasso-type cellwise regularizatio...

Full description

Saved in:
Bibliographic Details
Published inComputational statistics & data analysis Vol. 197; p. 107971
Main Authors Su, Peng, Tarr, Garth, Muller, Samuel, Wang, Suojin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2024
Subjects
Cellwise contamination
Cellwise regularization
Feature selection
Robust sparse regression
Cellwise contamination
Feature selection
Cellwise regularization
Robust sparse regression
Online AccessGet full text

Cover

More Information
Summary:Cellwise contamination remains a challenging problem for data scientists, particularly in research fields that require the selection of sparse features. Traditional robust methods may not be feasible nor efficient in dealing with such contaminated datasets. A robust Lasso-type cellwise regularization procedure is proposed which is coined CR-Lasso, that performs feature selection in the presence of cellwise outliers by minimising a regression loss and cell deviation measure simultaneously. The evaluation of this approach involves simulation studies that compare its selection and prediction performance with several sparse regression methods. The results demonstrate that CR-Lasso is competitive within the considered settings. The effectiveness of the proposed method is further illustrated through an analysis of a bone mineral density dataset.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2024.107971