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Bayesian Inference on the Type-II Extreme Value Distribution Parameters

Article scientifique 2024 Anglais

Résumé

Abstract In this paper, Bayesian inference is applied for estimating the type-II extreme value distribution parameters and reliability when the experimental data are collected under type-II progressive censored samples. Via Monte Carlo simulations, the statistical properties of the estimations under the non-informative prior (NIP) are compared with those under the informative prior (IP), when prior information on the unreliability level at a fixed time is introduced. This study has shown that the use of prior information outperforms the statistical properties of the estimations, even when only a modest amount of prior information on the process parameters is available. Finally, a numerical example is given to illustrate the inferential method developed in this paper.

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Maswadah, M. (2024). Bayesian Inference on the Type-II Extreme Value Distribution Parameters. https://doi.org/10.21203/rs.3.rs-3934835/v1

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Auteur(s)

M. Maswadah

Statistiques

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