Robust logistic regression in the presence of high leverage points
Author
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 11, Issue 3 (30 Sep. 2019), pp.1-11, 11 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2019-09-30
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Topics
Abstract EN
In this article we conceder the logistic regression model with high leverage points.
For the logistic regression model with a binary response, we suggested a new robust approach called robust logistic regression (RLR) based on the robust mahalanobis distance (RMD) which depends on the minimum volume ellipsoid (MVE) estimators.
The RMD is computed by using the algorithm of stochastic gradient descent (SGD).
In order to assist the new suggested approach we compare it with some existing method such as maximum likelihood estimator and robust M-estimator in logistic regression model.
The simulation study points that the RLR has supreme performances throw some measurement comparison.
American Psychological Association (APA)
Muhammad, Muhammad A.. 2019. Robust logistic regression in the presence of high leverage points. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 11, no. 3, pp.1-11.
https://search.emarefa.net/detail/BIM-900663
Modern Language Association (MLA)
Muhammad, Muhammad A.. Robust logistic regression in the presence of high leverage points. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 11, no. 3 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-900663
American Medical Association (AMA)
Muhammad, Muhammad A.. Robust logistic regression in the presence of high leverage points. al-Qadisiyah Journal for Computer Science and Mathematics. 2019. Vol. 11, no. 3, pp.1-11.
https://search.emarefa.net/detail/BIM-900663
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 10-11
Record ID
BIM-900663