Weighted high leverage collinear robust ridge estimator in logistic regression model

The combination of high leverage points and multicollinearity problem occurs frequently in logistic regression model. Methods that successfully address these problems separately are not effective for the combined problems. A robust logistic ridge regression (RLR) which incorporates the weighted Bian...

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Principais autores: Ariffin, Syaiba Balqish, Midi, Habshah
Formato: Artigo
Idioma:English
Publicado em: Pakistan Journal of Statistics 2018
Acesso em linha:http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf
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author Ariffin, Syaiba Balqish
Midi, Habshah
author_facet Ariffin, Syaiba Balqish
Midi, Habshah
author_sort Ariffin, Syaiba Balqish
collection UPM
description The combination of high leverage points and multicollinearity problem occurs frequently in logistic regression model. Methods that successfully address these problems separately are not effective for the combined problems. A robust logistic ridge regression (RLR) which incorporates the weighted Bianco and Yohai (WBY) robust estimator with fully iterated logistic ridge regression (LR) is proposed to rectify the combined problems of high leverage points and multicollinearity in a data. A numerical example and simulation study are presented to compare the performance of the RLR with the ML, the WBY, and the LR estimators. Results of the study indicate that the RLR outperforms the established estimators for the combined problems.
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spelling upm.eprints-744322024-09-11T01:53:55Z http://psasir.upm.edu.my/id/eprint/74432/ Weighted high leverage collinear robust ridge estimator in logistic regression model Ariffin, Syaiba Balqish Midi, Habshah The combination of high leverage points and multicollinearity problem occurs frequently in logistic regression model. Methods that successfully address these problems separately are not effective for the combined problems. A robust logistic ridge regression (RLR) which incorporates the weighted Bianco and Yohai (WBY) robust estimator with fully iterated logistic ridge regression (LR) is proposed to rectify the combined problems of high leverage points and multicollinearity in a data. A numerical example and simulation study are presented to compare the performance of the RLR with the ML, the WBY, and the LR estimators. Results of the study indicate that the RLR outperforms the established estimators for the combined problems. Pakistan Journal of Statistics 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf Ariffin, Syaiba Balqish and Midi, Habshah (2018) Weighted high leverage collinear robust ridge estimator in logistic regression model. Pakistan Journal of Statistics, 34 (1). pp. 55-75. ISSN 1012-9367; EISSN: 2310-3515 https://www.pakjs.com/wp-content/uploads/2019/09/34105.pdf
spellingShingle Ariffin, Syaiba Balqish
Midi, Habshah
Weighted high leverage collinear robust ridge estimator in logistic regression model
title Weighted high leverage collinear robust ridge estimator in logistic regression model
title_full Weighted high leverage collinear robust ridge estimator in logistic regression model
title_fullStr Weighted high leverage collinear robust ridge estimator in logistic regression model
title_full_unstemmed Weighted high leverage collinear robust ridge estimator in logistic regression model
title_short Weighted high leverage collinear robust ridge estimator in logistic regression model
title_sort weighted high leverage collinear robust ridge estimator in logistic regression model
url http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf
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