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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Main Authors: Ariffin, Syaiba Balqish, Midi, Habshah
Format: Article
Language:English
Published: Pakistan Journal of Statistics 2018
Online Access:http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf
http://psasir.upm.edu.my/id/eprint/74432/
https://www.pakjs.com/wp-content/uploads/2019/09/34105.pdf
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Institution: Universiti Putra Malaysia
Language: English
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spelling my.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Ariffin, Syaiba Balqish
Midi, Habshah
spellingShingle Ariffin, Syaiba Balqish
Midi, Habshah
Weighted high leverage collinear robust ridge estimator in logistic regression model
author_facet Ariffin, Syaiba Balqish
Midi, Habshah
author_sort Ariffin, Syaiba Balqish
title 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_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_sort weighted high leverage collinear robust ridge estimator in logistic regression model
publisher Pakistan Journal of Statistics
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf
http://psasir.upm.edu.my/id/eprint/74432/
https://www.pakjs.com/wp-content/uploads/2019/09/34105.pdf
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