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http://hdl.handle.net/20.500.12358/27120
TitleA comparison Study Between the Nadaraya-Watson and the Reweighted Nadaraya-Watson Estimators of the Regression Mean Function
Title in Arabicدراسة مقارنة بين مقدر ندارايا- واتسن ومقدر ندارايا- واتسن المرجح لدالة متوسط الانحدار
Abstract

Nonparametric kernel estimators are widely used in regression estimation. One of the most important kernel estimators of the regression mean function is the Nadaraya-Watson estimator. Another estimator, which is called the Reweighted Nadaraya-Watson estimator has been proposed to improve the performance of the Nadaraya- Watson estimator. In this paper, we have compared theoretically between the two estimators by looking at their asymptotic bias, variance and the mean squared error. The results of this comparison indicated that the bias of the Reweighted Nadaraya-Watson estimator is better than that of the Nadaraya-Watson estimator. Also, a comparison of the practical performance of the two estimators based on simulated and real data has been given. The results of this comparison was consistent with the results of the theoretical comparison and indicated that, the Reweighted Nadaraya-Watson estimator has modified the weakness of the Nadaraya-Watson estimator.

Authors
Salha, Raid B.
TypeJournal Article
Date2015
LanguageEnglish
Subjects
Kernel estimation
regression mean function
asymptotic bias
asymptotic variance
mean squared error
Published inJournal of Al Azhar University-Gaza (Natural Sciences)
Series17 : 85-102
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  • Staff Publications- Faculty of Science [910]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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