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http://hdl.handle.net/20.500.12358/21371
TitleOn the Kernel Estimation of the Regression Function
Title in Arabicحول تقدير النواة لدالة الانحدار
Abstract

In this study, we examined the relationship between the independent x and dependent y variables, and the axis of this thesis is focused on the method of how we can estimate the regression function, m(x), where m(x) = E(Y │ X = x). We used nonparametric estimation methods, using the local linear (LL) and the NadarayaWatson (NW) kernel estimators, to estimate the regression function, we derived the strong consistency and the asymptotic normality. Also, the optimal problem of bandwidth selection is studied for the two estimators. A theoretical comparison between the two estimators was given. The performance of the two estimators in estimating the regression function was tested using two simulated data. Finally the comparison results proved that local linear kernel estimator is better than Nadaraya-Watson estimators.

Authors
Samour, Mohammedd A.
Supervisors
Salha, Raid B.
Typeرسالة ماجستير
Date2015
LanguageEnglish
Publisherالجامعة الإسلامية - غزة
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  • PhD and MSc Theses- Faculty of Science [445]
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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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