Confidence regions for the multidimensional density in the uniform norm based on the recursive Wolverton-Wagner estimation
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909303154147328 |
|---|---|
| author | Formica, Maria Rosaria Ostrovsky, Eugeny Sirota, Leonid |
| author_facet | Formica, Maria Rosaria Ostrovsky, Eugeny Sirota, Leonid |
| contents | We construct an optimal exponential tail decreasing confidence region for an unknown density of distribution in the Lebesgue-Riesz as well as in the uniform} norm, built on the sample of the random vectors based of the famous recursive Wolverton-Wagner density estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01451 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Confidence regions for the multidimensional density in the uniform norm based on the recursive Wolverton-Wagner estimation Formica, Maria Rosaria Ostrovsky, Eugeny Sirota, Leonid Statistics Theory We construct an optimal exponential tail decreasing confidence region for an unknown density of distribution in the Lebesgue-Riesz as well as in the uniform} norm, built on the sample of the random vectors based of the famous recursive Wolverton-Wagner density estimation. |
| title | Confidence regions for the multidimensional density in the uniform norm based on the recursive Wolverton-Wagner estimation |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2409.01451 |