Shrinkage parameter selection via modified cross-validation approach for ridge regression model

The ridge regression estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The choice of the ridge shrinkage parameter is critical. Cross-validation method is a widely adopted method for shrinkage parameter selection. However, c...

Full description

Saved in:
Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 49; no. 7; pp. 1922 - 1930
Main Author Algamal, Zakariya Yahya
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.07.2020
Taylor & Francis Ltd
Subjects
Computer simulation
Cross-validation
Monte Carlo simulation
Multicollinearity
Parameter modification
Regression models
Ridge regression
Shrinkage
Online AccessGet full text

Cover

More Information
Summary:The ridge regression estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The choice of the ridge shrinkage parameter is critical. Cross-validation method is a widely adopted method for shrinkage parameter selection. However, cross-validation method suffers from instability in determining the best shrinkage parameter. To address this problem, a modification of the cross-validation method is proposed by repeating fold assignment. And then, a proper quantile value of the best shrinkage parameter values is utilized. Simulation and real data example results demonstrate that the proposed method is outperformed cross-validation and generalized cross-validation methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2018.1508704