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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Pakistan Journal of Statistics
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/74432/ http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf |
| _version_ | 1848857497005195264 |
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| author | Ariffin, Syaiba Balqish Midi, Habshah |
| author_facet | Ariffin, Syaiba Balqish Midi, Habshah |
| author_sort | Ariffin, Syaiba Balqish |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T11:58:29Z |
| format | Article |
| id | upm-74432 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:58:29Z |
| publishDate | 2018 |
| publisher | Pakistan Journal of Statistics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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/ http://psasir.upm.edu.my/id/eprint/74432/ http://psasir.upm.edu.my/id/eprint/74432/1/2018PJS.pdf |