Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13108
Title: Robust Nonparametric Regression Using Kernel Smoothing
Authors: ZHANG XIAOE
Keywords: Robust Nonparametric Regression using Kernel smoothing
Issue Date: 12-Mar-2008
Source: ZHANG XIAOE (2008-03-12). Robust Nonparametric Regression Using Kernel Smoothing. ScholarBank@NUS Repository.
Abstract: We consider the application of nonparametric kernel smoother with the presence of outliers. Previous researches in similar fields have been focused on the direct application of Huber's M-estimator in the nonparametric methods, which is both computationally and theoretically difficult. Besides, the traditional M-estimator defines a fixed cut-off value, which may not be efficient for data with non-constant variation. To address these problems, we adopt the idea of pseudo data, with the implementation of which, the robust estimate can be obtained from least squares kernel smoother. We also propose to select the cut-off value of the M-estimator according to the local variation of the data. Results from theoretical exercise show that the pseudo data and a least squares kernel smoother is equivalent to the classical robust kernel smoother. Simulation studies suggest that the robust kernel smoother with non-constant cut-off value leads to a superior mean squared error performance compared to fixed cut-off value.
URI: http://scholarbank.nus.edu.sg/handle/10635/13108
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
zhang_xiaoe.pdf489.78 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

213
checked on Dec 11, 2017

Download(s)

178
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.