Coefficient-Based Regression with Non-Identical Unbounded Sampling
We investigate a coefficient-based least squares regression problem with indefinite kernels from non-identical unbounded sampling processes. Here non-identical unbounded sampling means the samples are drawn independently but not identically from unbounded sampling processes. The kernel is not necess...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/134727 |
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| _version_ | 1832561085461299200 |
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| author | Jia Cai |
| author_facet | Jia Cai |
| author_sort | Jia Cai |
| collection | DOAJ |
| description | We investigate a coefficient-based least squares regression problem with indefinite kernels from non-identical unbounded sampling processes. Here non-identical unbounded sampling means the
samples are drawn independently but not identically from unbounded sampling processes. The kernel is not necessarily symmetric or positive semi-definite. This leads to additional difficulty in the error analysis. By introducing a suitable reproducing kernel Hilbert space (RKHS) and a suitable intermediate integral operator, elaborate analysis is presented by means of a novel technique for the sample error. This leads to satisfactory results. |
| format | Article |
| id | doaj-art-5a53e4e8de0846f08a09322ccb6b8726 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-5a53e4e8de0846f08a09322ccb6b87262025-02-03T01:25:57ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/134727134727Coefficient-Based Regression with Non-Identical Unbounded SamplingJia Cai0School of Mathematics and Computational Science, Guangdong University of Business Studies, Guangzhou, Guangdong 510320, ChinaWe investigate a coefficient-based least squares regression problem with indefinite kernels from non-identical unbounded sampling processes. Here non-identical unbounded sampling means the samples are drawn independently but not identically from unbounded sampling processes. The kernel is not necessarily symmetric or positive semi-definite. This leads to additional difficulty in the error analysis. By introducing a suitable reproducing kernel Hilbert space (RKHS) and a suitable intermediate integral operator, elaborate analysis is presented by means of a novel technique for the sample error. This leads to satisfactory results.http://dx.doi.org/10.1155/2013/134727 |
| spellingShingle | Jia Cai Coefficient-Based Regression with Non-Identical Unbounded Sampling Abstract and Applied Analysis |
| title | Coefficient-Based Regression with Non-Identical Unbounded Sampling |
| title_full | Coefficient-Based Regression with Non-Identical Unbounded Sampling |
| title_fullStr | Coefficient-Based Regression with Non-Identical Unbounded Sampling |
| title_full_unstemmed | Coefficient-Based Regression with Non-Identical Unbounded Sampling |
| title_short | Coefficient-Based Regression with Non-Identical Unbounded Sampling |
| title_sort | coefficient based regression with non identical unbounded sampling |
| url | http://dx.doi.org/10.1155/2013/134727 |
| work_keys_str_mv | AT jiacai coefficientbasedregressionwithnonidenticalunboundedsampling |