Bayesian Estimation of Hazard Rate of a Mixture Model With Censored Lifetimes

Ahn, Sun Eung; Park, Chang Soon; Kim, Hyun Mook; Kim, Woo Hyun

doi:10.1142/9789812773760_0068

Cited by 1 publication

(2 citation statements)

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“…where B(γ, Γ) is the beta function. The hyperparameters (γ, Γ, a j if j = 1, 2, 3, 4) are estimated by the same method given in [30]. Then, the joint PDF of hyperparameters for MO-GEVL could be written as…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Moreover, we explore Bayesian estimation using Lindley's method for the informative and non-informative priors. For the informative priors, the hyperparameters are determined following the approach initiated by [30] and subsequently employed by [34]. Additionally, an approximate 95% confidence interval for the parameters is calculated, offering a comprehensive statistical analysis based on the established methodologies.…”

Section: Simulationmentioning

confidence: 99%

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Estimation of Marshall–Olkin Extended Generalized Extreme Value Distribution Parameters under Progressive Type-II Censoring by Using a Genetic Algorithm

Attwa,

Sadk,

Radwan

2024

Symmetry

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In this article, we consider the statistical analysis of the parameter estimation of the Marshall–Olkin extended generalized extreme value under liner normalization distribution (MO-GEVL) within the context of progressively type-II censored data. The progressively type-II censored data are considered for three specific distribution patterns: fixed, discrete uniform, and binomial random removal. The challenge lies in the computation of maximum likelihood estimations (MLEs), as there is no straightforward analytical solution. The classical numerical methods are considered inadequate for solving the complex MLE equation system, leading to the necessity of employing artificial intelligence algorithms. This article utilizes the genetic algorithm (GA) to overcome this difficulty. This article considers parameter estimation through both maximum likelihood and Bayesian methods. For the MLE, the confidence intervals of the parameters are calculated using the Fisher information matrix. In the Bayesian estimation, the Lindley approximation is applied, considering LINEX loss functions and square error loss, suitable for both non-informative and informative contexts. The effectiveness and applicability of these proposed methods are demonstrated through numerical simulations and practical real-data examples.

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Section: Bayesian Estimationmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%