Comparation on Several Smoothing Methods in Nonparametric Regression

Isnanto, R.Rizal (2011) Comparation on Several Smoothing Methods in Nonparametric Regression. JURNAL SISTEM KOMPUTER , Vol.1 (No.1). pp. 41-47. ISSN 2087-4685

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Abstract

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.

Item Type:Article
Uncontrolled Keywords:nonparametric regression , smoothing, moving average, estimator, curve construction.
Subjects:T Technology > T Technology (General)
Divisions:Faculty of Engineering > Department of Computer System
Faculty of Engineering > Department of Computer System
ID Code:40499
Deposited By:Ms Melati mt
Deposited On:22 Nov 2013 11:49
Last Modified:22 Nov 2013 11:49

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