Variable selection in functional regression models: a review
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909083426095104 |
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| author | Aneiros, Germán Novo, Silvia Vieu, Philippe |
| author_facet | Aneiros, Germán Novo, Silvia Vieu, Philippe |
| contents | Despite of various similar features, Functional Data Analysis and High-Dimensional Data Analysis are two major fields in Statistics that grew up recently almost independently one from each other. The aim of this paper is to propose a survey on methodological advances for variable selection in functional regression, which is typically a question for which both functional and multivariate ideas are crossing. More than a simple survey, this paper aims to promote even more new links between both areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14867 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Variable selection in functional regression models: a review Aneiros, Germán Novo, Silvia Vieu, Philippe Methodology 62R10, 62H12 Despite of various similar features, Functional Data Analysis and High-Dimensional Data Analysis are two major fields in Statistics that grew up recently almost independently one from each other. The aim of this paper is to propose a survey on methodological advances for variable selection in functional regression, which is typically a question for which both functional and multivariate ideas are crossing. More than a simple survey, this paper aims to promote even more new links between both areas. |
| title | Variable selection in functional regression models: a review |
| topic | Methodology 62R10, 62H12 |
| url | https://arxiv.org/abs/2401.14867 |