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...
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| Published in: | Communications in statistics. Simulation and computation Vol. 49; no. 7; pp. 1922 - 1930 |
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| Format: | Journal Article |
| Language: | English |
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Taylor & Francis
02-07-2020
Taylor & Francis Ltd |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Algamal, Zakariya Yahya |
| Author_xml | – sequence: 1 givenname: Zakariya Yahya orcidid: 0000-0002-0229-7958 surname: Algamal fullname: Algamal, Zakariya Yahya email: zakariya.algamal@uomosul.edu.iq organization: Department of Statistics and Informatics, University of Mosul |
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| Cites_doi | 10.1080/03610920600762905 10.1080/03610920802503396 10.1080/03610918.2016.1144765 10.1080/01621459.1975.10479882 10.1016/j.econmod.2011.02.030 10.1002/cem.3054 10.1007/s00180-014-0504-3 10.1080/03610927508827232 10.1007/s00362-014-0596-4 10.1080/00401706.1979.10489751 10.1080/03610918.2014.995815 10.4236/ojs.2013.32011 10.1080/00949655.2015.1112392 10.1080/03610918.2012.735317 10.1080/03610910802592838 10.1111/j.2517-6161.1974.tb00994.x 10.1111/biom.12359 10.1111/1467-9574.00156 10.1080/03610918.2012.659821 10.1080/00401706.1970.10488634 10.1081/sta-200056836 10.1111/j.2517-6161.1996.tb02080.x 10.1081/sac-120017499 10.1080/03610926.2012.724506 |
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| Snippet | The ridge regression estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The choice... |
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| SubjectTerms | Computer simulation Cross-validation Monte Carlo simulation Multicollinearity Parameter modification Regression models Ridge regression Shrinkage |
| Title | Shrinkage parameter selection via modified cross-validation approach for ridge regression model |
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