An RKHS Framework for Sparse Functional Varying Coefficient Model
We study functional varying coefficient model in which both the response and the predictor are functions of a common variable such as time. We demonstrate the estimation of the slope function for the case of sparse and noise-contaminated longitudinal data. So far, a few methods have been introduced...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Instituto Nacional de Estatística | Statistics Portugal
2016-06-01
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| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/192 |
| _version_ | 1817998392844877824 |
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| author | Behdad Mostafaiy Mohammad Reza Faridrohani S. Mohammad E. Hosseininasab |
| author_facet | Behdad Mostafaiy Mohammad Reza Faridrohani S. Mohammad E. Hosseininasab |
| author_sort | Behdad Mostafaiy |
| collection | DOAJ |
| description |
We study functional varying coefficient model in which both the response and the predictor are functions of a common variable such as time. We demonstrate the estimation of the slope function for the case of sparse and noise-contaminated longitudinal data. So far, a few methods have been introduced based on varying coefficient model. To estimate the slope function, we consider a regularization method using a reproducing kernel Hilbert space framework. Despite the generality of the regularization method, the procedure is easy to implement. Our numerical results show that the introduced procedure performs well in some senses.
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| first_indexed | 2024-04-14T02:52:48Z |
| format | Article |
| id | doaj.art-b968dd1ea01b42f5ba53f7acf43a7a58 |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-04-14T02:52:48Z |
| publishDate | 2016-06-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-b968dd1ea01b42f5ba53f7acf43a7a582022-12-22T02:16:14ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712016-06-0114310.57805/revstat.v14i3.192An RKHS Framework for Sparse Functional Varying Coefficient ModelBehdad Mostafaiy 0Mohammad Reza Faridrohani 1S. Mohammad E. Hosseininasab2Shahid Beheshti UniversityShahid Beheshti UniversityShahid Beheshti University We study functional varying coefficient model in which both the response and the predictor are functions of a common variable such as time. We demonstrate the estimation of the slope function for the case of sparse and noise-contaminated longitudinal data. So far, a few methods have been introduced based on varying coefficient model. To estimate the slope function, we consider a regularization method using a reproducing kernel Hilbert space framework. Despite the generality of the regularization method, the procedure is easy to implement. Our numerical results show that the introduced procedure performs well in some senses. https://revstat.ine.pt/index.php/REVSTAT/article/view/192functional varying coefficient modelregularizationreproducing kernel Hilbert spacesparsity |
| spellingShingle | Behdad Mostafaiy Mohammad Reza Faridrohani S. Mohammad E. Hosseininasab An RKHS Framework for Sparse Functional Varying Coefficient Model Revstat Statistical Journal functional varying coefficient model regularization reproducing kernel Hilbert space sparsity |
| title | An RKHS Framework for Sparse Functional Varying Coefficient Model |
| title_full | An RKHS Framework for Sparse Functional Varying Coefficient Model |
| title_fullStr | An RKHS Framework for Sparse Functional Varying Coefficient Model |
| title_full_unstemmed | An RKHS Framework for Sparse Functional Varying Coefficient Model |
| title_short | An RKHS Framework for Sparse Functional Varying Coefficient Model |
| title_sort | rkhs framework for sparse functional varying coefficient model |
| topic | functional varying coefficient model regularization reproducing kernel Hilbert space sparsity |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/192 |
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