文章基本信息

标题：Comparation on Several Smoothing Methods in Nonparametric Regression
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作者：Isnanto, Rizal
期刊名称：Jurnal Sistem Komputer
印刷版ISSN：2252-3456
出版年度：2011
卷号：1
期号：1
页码：41-48
语种：English
出版社：Jurnal Sistem Komputer
摘要：There are three nonparametric regression methods covered in this section. These are Moving Average Filtering-Based Smoothing, Local Regression Smoothing, and Kernel Smoothing Methods. The Moving Average Filtering-Based Smoothing methods discussed here are Moving Average Filtering and Savitzky-Golay Filtering. While, the Local Regression Smoothing techniques involved here are Lowess and Loess. In this type of smoothing, Robust Smoothing and Upper-and-Lower Smoothing are also explained deeply, related to Lowess and Loess. Finally, the Kernel Smoothing Method involves three methods discussed. These are Nadaraya-Watson Estimator, Priestley-Chao Estimator, and Local Linear Kernel Estimator. The advantages of all above methods are discussed as well as the disadvantages of the methods.
关键词：nonparametric regression;smoothing;moving average;estimator;curve construction