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2007•Unpublished venueOpen access

A Comparison Of The Nonparametric Regression Models Using Smoothing Spline And Kernel Regression

Dursun Aydın

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Abstract

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression.

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What this paper is about

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression.

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OpenAlex reports 41 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression.

Key concepts: Nonparametric regression, Smoothing spline, Nonparametric statistics, Mathematics, Kernel smoother, Kernel regression, Semiparametric regression, Statistics

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