Robust Wavelet Regression With Automatic Boundary Correction

This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduce...

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Main Author:	Mohamed Altaher, Alsaidi Almahdi
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf
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id	my-usm-ep.60760
record_format	uketd_dc
spelling	my-usm-ep.607602024-06-26T03:10:03Z Robust Wavelet Regression With Automatic Boundary Correction 2012-12 Mohamed Altaher, Alsaidi Almahdi QA1-939 Mathematics This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduces five different robust methodologies to extend the validity of PWR and LPWR to describe data contaminated with outliers and independent noises. The second part pays special exception when the noise structure is correlated. 2012-12 Thesis http://eprints.usm.my/60760/ http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik ( School of Mathematical Sciences)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Mohamed Altaher, Alsaidi Almahdi Robust Wavelet Regression With Automatic Boundary Correction
description	This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduces five different robust methodologies to extend the validity of PWR and LPWR to describe data contaminated with outliers and independent noises. The second part pays special exception when the noise structure is correlated.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohamed Altaher, Alsaidi Almahdi
author_facet	Mohamed Altaher, Alsaidi Almahdi
author_sort	Mohamed Altaher, Alsaidi Almahdi
title	Robust Wavelet Regression With Automatic Boundary Correction
title_short	Robust Wavelet Regression With Automatic Boundary Correction
title_full	Robust Wavelet Regression With Automatic Boundary Correction
title_fullStr	Robust Wavelet Regression With Automatic Boundary Correction
title_full_unstemmed	Robust Wavelet Regression With Automatic Boundary Correction
title_sort	robust wavelet regression with automatic boundary correction
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik ( School of Mathematical Sciences)
publishDate	2012
url	http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf
_version_	1804888994937307136

Robust Wavelet Regression With Automatic Boundary Correction

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