Deriving Lehmer and Hölder means as maximum weighted likelihood estimates for the multivariate exponential family
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913339372732416 |
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| author | Ziou, Djemel Fakir, Issam |
| author_facet | Ziou, Djemel Fakir, Issam |
| contents | The links between the mean families of Lehmer and Hölder and the weighted maximum likelihood estimator have recently been established in the case of a regular univariate exponential family. In this article, we will extend the outcomes obtained to the multivariate case. This extension provides a probabilistic interpretation of these families of means and could therefore broaden their uses in various applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deriving Lehmer and Hölder means as maximum weighted likelihood estimates for the multivariate exponential family Ziou, Djemel Fakir, Issam Statistics Theory Machine Learning The links between the mean families of Lehmer and Hölder and the weighted maximum likelihood estimator have recently been established in the case of a regular univariate exponential family. In this article, we will extend the outcomes obtained to the multivariate case. This extension provides a probabilistic interpretation of these families of means and could therefore broaden their uses in various applications. |
| title | Deriving Lehmer and Hölder means as maximum weighted likelihood estimates for the multivariate exponential family |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2405.00964 |