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Comparation on Several Smoothing Methods in Nonparametric Regression

Author(s): Rizal Isnanto

Journal: Jurnal Sistem Komputer
ISSN 2087-4685

Volume: 1;
Issue: 1;
Start page: 41;
Date: 2011;
Original page

Keywords: nonparametric regression | smoothing | moving average | estimator | curve construction

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.
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