Robust logistic regression in the presence of high leverage points

المؤلف

Muhammad, Muhammad A.

المصدر

al-Qadisiyah Journal for Computer Science and Mathematics

العدد

المجلد 11، العدد 3 (30 سبتمبر/أيلول 2019)، ص ص. 1-11، 11ص.

الناشر

جامعة القادسية كلية علوم الحاسوب و تكنولوجيا المعلومات

تاريخ النشر

2019-09-30

دولة النشر

العراق

عدد الصفحات

11

التخصصات الرئيسية

الرياضيات

الموضوعات

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references : p. 10-11

رقم السجل

BIM-900663