The performance of classical and robust logistic regression estimators in the presence of outliers

The performance of classical and robust logistic regression estimators in the presence of outliers

It is now evident that the estimation of logistic regression parameters, using Maximum Likelihood Estimator(MLE), suffers a huge drawback in the presence of outliers. An alternative approach is to use robust logistic regression estimators, such as Mallows type leverage dependent weights estimator (...

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Main Authors: Midi, Habshah, Ariffin @ Mat Zin, Syaiba Balqish
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2012
Online Access:http://psasir.upm.edu.my/id/eprint/40467/
http://psasir.upm.edu.my/id/eprint/40467/
http://psasir.upm.edu.my/id/eprint/40467/1/16.%20The%20Performance%20of%20Classical%20and%20Robust%20Logistic%20Regression.pdf
id upm-40467
recordtype eprints
spelling upm-404672015-11-04T04:01:37Z http://psasir.upm.edu.my/id/eprint/40467/ The performance of classical and robust logistic regression estimators in the presence of outliers Midi, Habshah Ariffin @ Mat Zin, Syaiba Balqish It is now evident that the estimation of logistic regression parameters, using Maximum Likelihood Estimator(MLE), suffers a huge drawback in the presence of outliers. An alternative approach is to use robust logistic regression estimators, such as Mallows type leverage dependent weights estimator (MALLOWS, Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco and Yohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates the robustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. The results indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBY estimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBY estimator be employed when outliers are present in the data to obtain a reliable estimate. Universiti Putra Malaysia Press 2012 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/40467/1/16.%20The%20Performance%20of%20Classical%20and%20Robust%20Logistic%20Regression.pdf Midi, Habshah and Ariffin @ Mat Zin, Syaiba Balqish (2012) The performance of classical and robust logistic regression estimators in the presence of outliers. Pertanika Journal of Science & Technology, 20 (2). pp. 313-325. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2020%20%282%29%20Jul.%202012/09%20Pg%20313-325.pdf
repository_type Digital Repository
institution_category Local University
institution Universiti Putra Malaysia
building UPM Institutional Repository
collection Online Access
language English
description It is now evident that the estimation of logistic regression parameters, using Maximum Likelihood Estimator(MLE), suffers a huge drawback in the presence of outliers. An alternative approach is to use robust logistic regression estimators, such as Mallows type leverage dependent weights estimator (MALLOWS, Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco and Yohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates the robustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. The results indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBY estimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBY estimator be employed when outliers are present in the data to obtain a reliable estimate.
format Article
author Midi, Habshah
Ariffin @ Mat Zin, Syaiba Balqish
spellingShingle Midi, Habshah
Ariffin @ Mat Zin, Syaiba Balqish
The performance of classical and robust logistic regression estimators in the presence of outliers
author_facet Midi, Habshah
Ariffin @ Mat Zin, Syaiba Balqish
author_sort Midi, Habshah
title The performance of classical and robust logistic regression estimators in the presence of outliers
title_short The performance of classical and robust logistic regression estimators in the presence of outliers
title_full The performance of classical and robust logistic regression estimators in the presence of outliers
title_fullStr The performance of classical and robust logistic regression estimators in the presence of outliers
title_full_unstemmed The performance of classical and robust logistic regression estimators in the presence of outliers
title_sort performance of classical and robust logistic regression estimators in the presence of outliers
publisher Universiti Putra Malaysia Press
publishDate 2012
url http://psasir.upm.edu.my/id/eprint/40467/
http://psasir.upm.edu.my/id/eprint/40467/
http://psasir.upm.edu.my/id/eprint/40467/1/16.%20The%20Performance%20of%20Classical%20and%20Robust%20Logistic%20Regression.pdf
first_indexed 2018-09-07T17:00:51Z
last_indexed 2018-09-07T17:00:51Z
_version_ 1610968887049846784

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