Optimal multiple functional regression analysis using perturbation theory
A regression analysis is presented based on several functions. A normalized data are used which enables the usage of magnitude of orders in perturbation theory. The criteria to eliminate unnecessary base functions are derived with the aid of order of magnitudes in perturbations. The analysis general...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Taibah University for Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/16583655.2024.2366522 |
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| _version_ | 1846118725665685504 |
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| author | Mehmet Pakdemirli |
| author_facet | Mehmet Pakdemirli |
| author_sort | Mehmet Pakdemirli |
| collection | DOAJ |
| description | A regression analysis is presented based on several functions. A normalized data are used which enables the usage of magnitude of orders in perturbation theory. The criteria to eliminate unnecessary base functions are derived with the aid of order of magnitudes in perturbations. The analysis generalizes the previous work on polynomial regression of arbitrary orders. Properties of the regression coefficients are outlined via theorems. Numerical tests are conducted to outline the usage of the analysis to eliminate unnecessary functions and obtain a reasonable approximation of the data in a continuous form. |
| format | Article |
| id | doaj-art-2cf7587f2f8f4bd1a78bc3e25b7a862c |
| institution | Kabale University |
| issn | 1658-3655 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Taibah University for Science |
| spelling | doaj-art-2cf7587f2f8f4bd1a78bc3e25b7a862c2024-12-17T11:38:48ZengTaylor & Francis GroupJournal of Taibah University for Science1658-36552024-12-0118110.1080/16583655.2024.2366522Optimal multiple functional regression analysis using perturbation theoryMehmet Pakdemirli0Department of Mechanical Engineering, Manisa Celal Bayar University, Muradiye, TurkeyA regression analysis is presented based on several functions. A normalized data are used which enables the usage of magnitude of orders in perturbation theory. The criteria to eliminate unnecessary base functions are derived with the aid of order of magnitudes in perturbations. The analysis generalizes the previous work on polynomial regression of arbitrary orders. Properties of the regression coefficients are outlined via theorems. Numerical tests are conducted to outline the usage of the analysis to eliminate unnecessary functions and obtain a reasonable approximation of the data in a continuous form.https://www.tandfonline.com/doi/10.1080/16583655.2024.2366522Functional regressionperturbation analysisdata approximation |
| spellingShingle | Mehmet Pakdemirli Optimal multiple functional regression analysis using perturbation theory Journal of Taibah University for Science Functional regression perturbation analysis data approximation |
| title | Optimal multiple functional regression analysis using perturbation theory |
| title_full | Optimal multiple functional regression analysis using perturbation theory |
| title_fullStr | Optimal multiple functional regression analysis using perturbation theory |
| title_full_unstemmed | Optimal multiple functional regression analysis using perturbation theory |
| title_short | Optimal multiple functional regression analysis using perturbation theory |
| title_sort | optimal multiple functional regression analysis using perturbation theory |
| topic | Functional regression perturbation analysis data approximation |
| url | https://www.tandfonline.com/doi/10.1080/16583655.2024.2366522 |
| work_keys_str_mv | AT mehmetpakdemirli optimalmultiplefunctionalregressionanalysisusingperturbationtheory |