Roughness regularization for functional data analysis with free knots spline estimation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917807697952768 |
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| author | De Magistris, Anna De Simone, Valentina Romano, Elvira Toraldo, Gerardo |
| author_facet | De Magistris, Anna De Simone, Valentina Romano, Elvira Toraldo, Gerardo |
| contents | In the era of big data, an ever-growing volume of information is recorded, either continuously over time or sporadically, at distinct time intervals. Functional Data Analysis (FDA) stands at the cutting edge of this data revolution, offering a powerful framework for handling and extracting meaningful insights from such complex datasets. The currently proposed FDA me\-thods can often encounter challenges, especially when dealing with curves of varying shapes. This can largely be attributed to the method's strong dependence on data approximation as a key aspect of the analysis process. In this work, we propose a free knots spline estimation method for functional data with two penalty terms and demonstrate its performance by comparing the results of several clustering methods on simulated and real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Roughness regularization for functional data analysis with free knots spline estimation De Magistris, Anna De Simone, Valentina Romano, Elvira Toraldo, Gerardo Methodology Numerical Analysis Applications In the era of big data, an ever-growing volume of information is recorded, either continuously over time or sporadically, at distinct time intervals. Functional Data Analysis (FDA) stands at the cutting edge of this data revolution, offering a powerful framework for handling and extracting meaningful insights from such complex datasets. The currently proposed FDA me\-thods can often encounter challenges, especially when dealing with curves of varying shapes. This can largely be attributed to the method's strong dependence on data approximation as a key aspect of the analysis process. In this work, we propose a free knots spline estimation method for functional data with two penalty terms and demonstrate its performance by comparing the results of several clustering methods on simulated and real data. |
| title | Roughness regularization for functional data analysis with free knots spline estimation |
| topic | Methodology Numerical Analysis Applications |
| url | https://arxiv.org/abs/2407.05159 |