Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909105267933184 |
|---|---|
| author | Imai, Shunsuke Koriyama, Takuya Yonekura, Shouto Sugasawa, Shonosuke Nishiyama, Yoshihiko |
| author_facet | Imai, Shunsuke Koriyama, Takuya Yonekura, Shouto Sugasawa, Shonosuke Nishiyama, Yoshihiko |
| contents | We introduce a new deal of kernel density estimation using an exponentiated form of kernel density estimators. The density estimator has two hyperparameters flexibly controlling the smoothness of the resulting density. We tune them in a data-driven manner by minimizing an objective function based on the Hyvärinen score to avoid the optimization involving the intractable normalizing constant due to the exponentiation. We show the asymptotic properties of the proposed estimator and emphasize the importance of including the two hyperparameters for flexible density estimation. Our simulation studies and application to income data show that the proposed density estimator is appealing when the underlying density is multi-modal or observations contain outliers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_00984 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score Imai, Shunsuke Koriyama, Takuya Yonekura, Shouto Sugasawa, Shonosuke Nishiyama, Yoshihiko Methodology We introduce a new deal of kernel density estimation using an exponentiated form of kernel density estimators. The density estimator has two hyperparameters flexibly controlling the smoothness of the resulting density. We tune them in a data-driven manner by minimizing an objective function based on the Hyvärinen score to avoid the optimization involving the intractable normalizing constant due to the exponentiation. We show the asymptotic properties of the proposed estimator and emphasize the importance of including the two hyperparameters for flexible density estimation. Our simulation studies and application to income data show that the proposed density estimator is appealing when the underlying density is multi-modal or observations contain outliers. |
| title | Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score |
| topic | Methodology |
| url | https://arxiv.org/abs/2212.00984 |