Constrained recursive kernel density/regression estimation by stochastic quasi-gradient methods
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917767868841984 |
|---|---|
| author | Norkin, Vladimir Kirilyuk, Vladimir |
| author_facet | Norkin, Vladimir Kirilyuk, Vladimir |
| contents | The paper considers nonparametric kernel density/regression estimation from a stochastic optimization point of view. The estimation problem is represented through a family of stochastic optimization problems. Recursive constrained estimators are obtained by application of stochastic (quasi)gradient methods to these problems, classical kernel estimates are derived as particular cases. Accuracy and rate of convergence of the obtained estimates are established, and asymptotically optimal estimation procedure parameters are found. The case of moving density/regression is particularly studied. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constrained recursive kernel density/regression estimation by stochastic quasi-gradient methods Norkin, Vladimir Kirilyuk, Vladimir Statistics Theory Optimization and Control The paper considers nonparametric kernel density/regression estimation from a stochastic optimization point of view. The estimation problem is represented through a family of stochastic optimization problems. Recursive constrained estimators are obtained by application of stochastic (quasi)gradient methods to these problems, classical kernel estimates are derived as particular cases. Accuracy and rate of convergence of the obtained estimates are established, and asymptotically optimal estimation procedure parameters are found. The case of moving density/regression is particularly studied. |
| title | Constrained recursive kernel density/regression estimation by stochastic quasi-gradient methods |
| topic | Statistics Theory Optimization and Control |
| url | https://arxiv.org/abs/2406.16550 |