On the estimation of the number of components in multivariate functional principal component analysis
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917902727249920 |
|---|---|
| author | Golovkine, Steven Gunning, Edward Simpkin, Andrew J. Bargary, Norma |
| author_facet | Golovkine, Steven Gunning, Edward Simpkin, Andrew J. Bargary, Norma |
| contents | Happ and Greven (2018) developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this paper, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04540 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the estimation of the number of components in multivariate functional principal component analysis Golovkine, Steven Gunning, Edward Simpkin, Andrew J. Bargary, Norma Methodology Machine Learning 62R10 Happ and Greven (2018) developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this paper, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution. |
| title | On the estimation of the number of components in multivariate functional principal component analysis |
| topic | Methodology Machine Learning 62R10 |
| url | https://arxiv.org/abs/2311.04540 |