Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913297933008896 |
|---|---|
| author | Janssen, Joseph Meng, Shizhe Haris, Asad Schrunner, Stefan Cao, Jiguo Welch, William J. Kunz, Nadja Ameli, Ali A. |
| author_facet | Janssen, Joseph Meng, Shizhe Haris, Asad Schrunner, Stefan Cao, Jiguo Welch, William J. Kunz, Nadja Ameli, Ali A. |
| contents | Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable limitations exist. Most importantly, previous works have imposed sparsity on the historical coefficient function, but have not allowed the sparsity, hence lag, to vary with time. We simplify the framework of sparse functional historical linear models by using a rectangular coefficient structure along with Whittaker smoothing, then reduce the assumptions of the previous frameworks by estimating the dynamic time lag from a hierarchical coefficient structure. We motivate our study by aiming to extract the physical rainfall-runoff processes hidden within hydrological data. We show the promise and accuracy of our method using eight simulation studies, further justified by two real sets of hydrological data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_06501 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science Janssen, Joseph Meng, Shizhe Haris, Asad Schrunner, Stefan Cao, Jiguo Welch, William J. Kunz, Nadja Ameli, Ali A. Methodology Applications Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable limitations exist. Most importantly, previous works have imposed sparsity on the historical coefficient function, but have not allowed the sparsity, hence lag, to vary with time. We simplify the framework of sparse functional historical linear models by using a rectangular coefficient structure along with Whittaker smoothing, then reduce the assumptions of the previous frameworks by estimating the dynamic time lag from a hierarchical coefficient structure. We motivate our study by aiming to extract the physical rainfall-runoff processes hidden within hydrological data. We show the promise and accuracy of our method using eight simulation studies, further justified by two real sets of hydrological data. |
| title | Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2303.06501 |