Efficient regression estimators for ordinary least squares and Jackknife based on Jackknife algorithm by deleting one case
Author
Source
Al Kut Journal of Economic and Administrative Sciences
Issue
Vol. 2017, Issue 26 (30 Jun. 2017)8 p.
Publisher
University of Wasit College of Administration and Economics
Publication Date
2017-06-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Topics
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
-
Record ID
BIM-1208660
