A Comparison of the Nonparametric Regression Models using Smoothing Spline and Kernel Regression

被引：0

作者：

Aydin, Dursun ^{[1
]}

机构：

[1] Anadolu Univ, Eskisehir, Turkey

来源：

PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 26, PARTS 1 AND 2, DECEMBER 2007 | 2007年 / 26卷

关键词：

Kernel regression; Nonparametric models; Prediction; Smoothing spline;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：730 / 734

页数：5

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