Uncovering Data Across Continua: An Introduction to Functional Data Analysis
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929327501737984 |
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| author | Dabo-Niang, Sophie Frévent, Camille |
| author_facet | Dabo-Niang, Sophie Frévent, Camille |
| contents | In a world increasingly awash with data, the need to extract meaningful insights from data has never been more crucial. Functional Data Analysis (FDA) goes beyond traditional data points, treating data as dynamic, continuous functions, capturing ever-changing phenomena nuances. This article introduces FDA, merging statistics with real-world complexity, ideal for those with mathematical skills but no FDA background. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncovering Data Across Continua: An Introduction to Functional Data Analysis Dabo-Niang, Sophie Frévent, Camille Statistics Theory In a world increasingly awash with data, the need to extract meaningful insights from data has never been more crucial. Functional Data Analysis (FDA) goes beyond traditional data points, treating data as dynamic, continuous functions, capturing ever-changing phenomena nuances. This article introduces FDA, merging statistics with real-world complexity, ideal for those with mathematical skills but no FDA background. |
| title | Uncovering Data Across Continua: An Introduction to Functional Data Analysis |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2404.16598 |