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Robust bootstrap methods in logistic regression model


Ariffin, Syaiba Balqish and Midi, Habshah (2012) Robust bootstrap methods in logistic regression model. In: 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE 2012), 10-12 Sept. 2012, Langkawi, Kedah. .


Bootstrapping is rapidly becoming a popular alternative tool to estimate coefficients and standard errors for logistic regression model. It is now evident that the presence of high leverage points give adverse effect on the classical bootstrap (CB) estimates as its highly dependent on the classical maximum likelihood estimator (MLE). In this paper, we propose two robust bootstrap methods, namely the diagnostic logistic before bootstrap (DLGBB) and the weighted logistic bootstrap with probability (WLGBP) to remedy the effect of high leverage points on bootstrap estimates. The conceptual behind the DLGBB method is to apply resampling with the remaining good observations. Meanwhile, in the WLGBP method probability selection procedure is formulated by assigning lower probability to high leverage points. Medical real data sets are employed to evaluate the performance of the DLGBB and the WLGBP estimates as compared to the CB estimates. The findings signify that the DLGBB is the most efficient method followed by the WLGBP.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Science
DOI Number: https://doi.org/10.1109/ICSSBE.2012.6396613
Publisher: IEEE
Notes: Full text are available at Special Collection Division Office.
Keywords: Logistic regression; High leverage points; Maximum likelihood estimator; Random-X resampling; Robust bootstrap
Depositing User: Samsida Samsudin
Date Deposited: 04 Apr 2014 04:33
Last Modified: 29 Jun 2020 02:00
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICSSBE.2012.6396613
URI: http://psasir.upm.edu.my/id/eprint/27631
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