Applications of the Quantile-Based Probabilistic Mean Value Theorem to Distorted Distributions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909446036258816 |
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| author | Di Crescenzo, Antonio Martinucci, Barbara Mulero, Julio |
| author_facet | Di Crescenzo, Antonio Martinucci, Barbara Mulero, Julio |
| contents | Distorted distributions were introduced in the context of actuarial science for several variety of insurance problems. In this paper we consider the quantile-based probabilistic mean value theorem given in Di Crescenzo et al. [4] and provide some applications based on distorted random variables. Specifically, we consider the cases when the underlying random variables satisfy the proportional hazard rate model and the proportional reversed hazard rate model. A setting based on random variables having the 'new better than used' property is also analyzed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00362 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Applications of the Quantile-Based Probabilistic Mean Value Theorem to Distorted Distributions Di Crescenzo, Antonio Martinucci, Barbara Mulero, Julio Probability Distorted distributions were introduced in the context of actuarial science for several variety of insurance problems. In this paper we consider the quantile-based probabilistic mean value theorem given in Di Crescenzo et al. [4] and provide some applications based on distorted random variables. Specifically, we consider the cases when the underlying random variables satisfy the proportional hazard rate model and the proportional reversed hazard rate model. A setting based on random variables having the 'new better than used' property is also analyzed. |
| title | Applications of the Quantile-Based Probabilistic Mean Value Theorem to Distorted Distributions |
| topic | Probability |
| url | https://arxiv.org/abs/2501.00362 |