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...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English |
| 出版: |
2012
|
| 主題: | |
| 在線閱讀: | http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf http://eprints.usm.my/60760/ |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| id |
my.usm.eprints.60760 |
|---|---|
| record_format |
eprints |
| spelling |
my.usm.eprints.60760 http://eprints.usm.my/60760/ Robust Wavelet Regression With Automatic Boundary Correction 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 NonPeerReviewed application/pdf en http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf Mohamed Altaher, Alsaidi Almahdi (2012) Robust Wavelet Regression With Automatic Boundary Correction. PhD thesis, Universiti Sains Malaysia. |
| institution |
Universiti Sains Malaysia |
| building |
Hamzah Sendut Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Sains Malaysia |
| content_source |
USM Institutional Repository |
| url_provider |
http://eprints.usm.my/ |
| 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 |
| 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 |
| publishDate |
2012 |
| url |
http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf http://eprints.usm.my/60760/ |
| _version_ |
1802977909038645248 |
| score |
13.251813 |