Efficient regression estimators for ordinary least squares and Jackknife based on Jackknife algorithm by deleting one case

المؤلف

Ali, Fayyad Abdullah

المصدر

Al Kut Journal of Economic and Administrative Sciences

العدد

المجلد 2017، العدد 26 (30 يونيو/حزيران 2017)8ص.

الناشر

جامعة واسط كلية الإدارة و الاقتصاد

تاريخ النشر

2017-06-30

دولة النشر

العراق

عدد الصفحات

8

التخصصات الرئيسية

الاقتصاد و التجارة

الموضوعات

الملخص EN

The aim of the research is to find an OLS estimator based on the Jackknife method by deleting one case that is more efficient than OLS estimator and Jackknife estimator.

Jackknife estimators and algorithms were reviewed.

The researcher found that the proposed estimator is better than OLS estimator and Jackknife estimators depending on MSE as comparison criterion.

The aim of the research is to find an OLS estimator based on the Jackknife method by deleting one case that is more efficient than OLS estimator and Jackknife estimator.

Jackknife estimators and algorithms were reviewed.

The researcher found that the proposed estimator is better than OLS estimator and Jackknife estimators depending on MSE as comparison criterion.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Ali, Fayyad Abdullah. 2017. Efficient regression estimators for ordinary least squares and Jackknife based on Jackknife algorithm by deleting one case. Al Kut Journal of Economic and Administrative Sciences،Vol. 2017, no. 26.
https://search.emarefa.net/detail/BIM-1208660

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Ali, Fayyad Abdullah. Efficient regression estimators for ordinary least squares and Jackknife based on Jackknife algorithm by deleting one case. Al Kut Journal of Economic and Administrative Sciences No. 26 (Jun. 2017).
https://search.emarefa.net/detail/BIM-1208660

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Ali, Fayyad Abdullah. Efficient regression estimators for ordinary least squares and Jackknife based on Jackknife algorithm by deleting one case. Al Kut Journal of Economic and Administrative Sciences. 2017. Vol. 2017, no. 26.
https://search.emarefa.net/detail/BIM-1208660

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

-

رقم السجل

BIM-1208660