Wasserstein and weighted metrics for multidimensional Gaussian distributions
We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and l...
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| Autor*in: |
Kelbert, Mark Yakovlevich [verfasserIn] Suhov, Yurii M. [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch ; Russisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Известия Саратовского университета. Новая серия. Серия Математика. Механика. Информатика - Saratov State University, 2019, 23(2023), 4, Seite 422-434 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:4 ; pages:422-434 |
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Link aufrufen |
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10.18500/1816-9791-2023-23-4-422-434 |
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DOAJ100949460 |
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| 520 | |a We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved. | ||
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Wasserstein and weighted metrics for multidimensional Gaussian distributions |
| abstract |
We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved. |
| abstractGer |
We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved. |
| abstract_unstemmed |
We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved. |
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GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ |
| container_issue |
4 |
| title_short |
Wasserstein and weighted metrics for multidimensional Gaussian distributions |
| url |
https://doi.org/10.18500/1816-9791-2023-23-4-422-434 https://doaj.org/article/9ac7c1a6adf54ab28103a50ee05d7912 https://mmi.sgu.ru/sites/mmi.sgu.ru/files/text-pdf/2023/11/422-434-kelbert-suhov.pdf https://doaj.org/toc/1816-9791 https://doaj.org/toc/2541-9005 |
| remote_bool |
true |
| author2 |
Suhov, Yurii M. |
| author2Str |
Suhov, Yurii M. |
| ppnlink |
1760618233 |
| callnumber-subject |
QA - Mathematics |
| mediatype_str_mv |
c |
| isOA_txt |
true |
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false |
| doi_str |
10.18500/1816-9791-2023-23-4-422-434 |
| callnumber-a |
QA1-939 |
| up_date |
2024-07-03T17:38:31.209Z |
| _version_ |
1803580406457434112 |
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7.402231 |