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Bayesian estimation of the Pareto model based on type-II censoring data by employing non-linear programming.
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- Author(s): AL-Essa, Laila A.; Al-Duais, Fuad S.; Aydi, Walid; AL-Rezami, Afrah Y.
- Source:
Alexandria Engineering Journal; Jan2024, Vol. 87, p398-403, 6p- Subject Terms:
- Source:
- Additional Information
- Abstract: The main goal of this article is to determine the optimally weighted coefficients (Ω 1 and Ω 2) of the balanced loss function of the form. L Κ , Ω , ξ o Ψ (σ) , ξ = Ω 1 γ σ Κ ξ o , ξ + Ω 2 γ σ Κ Ψ (σ) , ξ ; Ω 1 + Ω 2 = 1. Based on Type II Censored Data, by applying non-linear programming to estimate the shape parameter and some survival time characteristics, such as reliability and hazard functions of the Pareto distribution. Considering two balanced loss functions (BLF), including balanced square error loss function (BSELF) and balanced linear exponential loss function (BLLF), the calculation is based on the balanced loss function, including symmetric and asymmetric loss functions, as a special case. Use Monte Carlo simulation to pass Bayesian and maximum likelihood estimators through. The results of the simulation showed that the proposed model BLLF has the best performance. Moreover, the simulation verified that the balanced loss functions are always better than the corresponding loss function. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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