Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.22884 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911411014205440 |
|---|---|
| author | Arribas-Gil, Ana López-Pintado, Sara |
| author_facet | Arribas-Gil, Ana López-Pintado, Sara |
| contents | In the context of multivariate functional data with individual phase variation, we develop a robust depth-based approach to estimate the main pattern function when cross-component time warping is also present. In particular, we consider the latent deformation model (Carroll and Müller, 2023) in which the different components of a multivariate functional variable are also time-distorted versions of a common template function. Rather than focusing on a particular functional depth measure, we discuss the necessary conditions on a depth function to be able to provide a consistent estimation of the central pattern, considering different model assumptions. We evaluate the method performance and its robustness against atypical observations and violations of the model assumptions through simulations, and illustrate its use on two real data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22884 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Depth-based estimation for multivariate functional data with phase variability Arribas-Gil, Ana López-Pintado, Sara Methodology Computation In the context of multivariate functional data with individual phase variation, we develop a robust depth-based approach to estimate the main pattern function when cross-component time warping is also present. In particular, we consider the latent deformation model (Carroll and Müller, 2023) in which the different components of a multivariate functional variable are also time-distorted versions of a common template function. Rather than focusing on a particular functional depth measure, we discuss the necessary conditions on a depth function to be able to provide a consistent estimation of the central pattern, considering different model assumptions. We evaluate the method performance and its robustness against atypical observations and violations of the model assumptions through simulations, and illustrate its use on two real data sets. |
| title | Depth-based estimation for multivariate functional data with phase variability |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2601.22884 |