Complexity Analysis of E-Bayesian Estimation under Type-II Censoring with Application to Organ Transplant Blood Data

Nassar, Mazen; Alotaibi, Refah; Elshahhat, Ahmed

doi:10.3390/sym14071308

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…This application is concerned with analyzing a real-world dataset of 56 blood specimens from organ transplant patients. This dataset was originally offered by Hawkins [23] and was then studied by Nassar et al [24]. The organ transplant blood (OTB) data are presented in Table 5.…”

Section: Pathology Data Analysismentioning

confidence: 99%

Reliability Analysis and Its Applications for a Newly Improved Type-II Adaptive Progressive Alpha Power Exponential Censored Sample

Elbatal,

Nassar,

Ben Ghorbal

et al. 2023

Symmetry

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Recently, a newly improved Type-II adaptive progressive censoring plan was devised, which can successfully ensure that the test length will not surpass a particular threshold period. In this study, we explore the statistical inference of the alpha power exponential distribution in the context of improved adaptive progressive Type-II censored data. The parameters, reliability, and hazard functions were estimated from both classical and Bayesian viewpoints using this censoring plan. To begin, we applied the maximum likelihood estimation approach to obtain parameter, reliability, and hazard function estimators. Following that, the approximate confidence intervals for the aforementioned metrics were derived, assuming the asymptotic normality traits of the maximum likelihood estimators. Additionally, by employing the Bayesian method via the Markov chain Monte Carlo technique, the point estimators and highest posterior density intervals of various parameters were created based on the symmetric squared error loss. A simulation study that incorporates numerous scenarios was used to assess the effectiveness of various estimation methodologies. The optimal progressive censorship plans are then discussed based on a set of criteria. Finally, three applications from the engineering and medical domains have been offered as examples.

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Section: Pathology Data Analysismentioning

confidence: 99%

Reliability Analysis and Its Applications for a Newly Improved Type-II Adaptive Progressive Alpha Power Exponential Censored Sample

Elbatal,

Nassar,

Ben Ghorbal

et al. 2023

Symmetry

Self Cite

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“…Basheer et al [13] presented hierarchical and E-Bayesian estimations for the inverse Weibull model and concluded that the E-Bayesian was better than the other compared methods. Nassar et al [14] used the E-Bayesian and Bayesian estimation approaches to estimate the generalized inverted exponential distribution parameters under type-II censored data. Mohie El-din et al [15] estimated the parameters of the Gompertz distribution under diferent types of LFs using E-Bayesian estimation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and E-Bayesian Estimation for the Generalized Rayleigh Distribution under Different Forms of Loss Functions with Real Data Application

Eldemery,

Abd-Elfattah,

Mahfouz

et al. 2023

Journal of Mathematics

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This paper investigates the estimation of an unknown shape parameter of the generalized Rayleigh distribution using Bayesian and expected Bayesian estimation techniques based on type-II censoring data. Subsequently, these estimators are obtained using four different loss functions: the linear exponential loss function, the weighted linear exponential loss function, the compound linear exponential loss function, and the weighted compound linear exponential loss function. The weighted compound linear exponential loss function is a novel suggested loss function generated by combining weights with the compound linear exponential loss function. We use the gamma distribution as a prior distribution. In addition, the expected Bayesian estimator is obtained through three different prior distributions of the hyperparameters. Moreover, depending on the four distinct forms of loss functions, Bayesian and expected Bayesian estimation techniques are performed using Monte Carlo simulations to verify the effectiveness of the suggested loss function and to compare Bayesian and expected Bayesian estimation methods. Furthermore, the simulation results indicate that, depending on the minimum mean squared error, the Bayesian and expected Bayesian estimations corresponding to the weighted compound linear exponential loss function suggested in this paper have significantly better performance compared to other loss functions, and the expected Bayesian estimator also performs better than the Bayesian estimator. Finally, the proposed techniques are demonstrated using a set of real data from the medical field to clarify the applicability of the suggested estimators to real phenomena and to show that the discussed weighted compound linear exponential loss function is efficient and can be applied in a real-life scenario.

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Empirical E-Bayesian estimation of hierarchical poisson and gamma model using scaled squared error loss function

Iqbal¹,

Shad²,

Yassen³

2023

Alexandria Engineering Journal

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Complexity Analysis of E-Bayesian Estimation under Type-II Censoring with Application to Organ Transplant Blood Data

Cited by 6 publications

References 37 publications

Reliability Analysis and Its Applications for a Newly Improved Type-II Adaptive Progressive Alpha Power Exponential Censored Sample

Reliability Analysis and Its Applications for a Newly Improved Type-II Adaptive Progressive Alpha Power Exponential Censored Sample

Bayesian and E-Bayesian Estimation for the Generalized Rayleigh Distribution under Different Forms of Loss Functions with Real Data Application

Empirical E-Bayesian estimation of hierarchical poisson and gamma model using scaled squared error loss function

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