Comparation on Several Smoothing Methods in Nonparametric Regression
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.
Author: R. Rizal Isnanto
Journal Code: jptkomputergg110002
