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Robust Wavelet Regression With Automatic Boundary Correction

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dc.contributor.authorAlsaidi Almahdi Mohamed Altaher
dc.date.accessioned2024-06-26T03:08:00Z
dc.date.available2024-06-26T03:08:00Z
dc.date.issued2012-12
dc.description.abstractThis 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.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/19516
dc.subjectRobust Wavelet Regression
dc.subjectAutomatic Boundary Correction
dc.titleRobust Wavelet Regression With Automatic Boundary Correction
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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Pusat Pengajian Sains Matematik - Tesis