Bayesian Prediction Bounds for the Exponential-type Distribution Based on Generalized Progressive Hybrid Censoring Scheme

Kotb, Mohammed S.

doi:10.1515/eqc-2018-0012

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…where 𝜑(𝑥 𝑅 (𝑖) ; 𝛽, 𝜆) = (1 + 𝑅 𝑖 ) ( Clearly, from (10) to (11), we have a system of two nonlinear equations that must be solved simultaneously to obtain the desired MLEs β and λ of 𝛽 and 𝜆, respectively. Thus, the "maxLik" package, proposed, elsewhere 33 which implements the Newton-Raphson iterative of maximization in the computations is considered.…”

Section: Likelihood Inferencementioning

confidence: 99%

“…In recent literature, several authors have considered this scheme using some lifetime distributions, among others see for example. [6][7][8][9][10][11][12][13][14][15] A new two-parameter extended exponential distribution was introduced elsewhere 16 and was recently named NHD as an abbreviation of the author's name. Suppose that the lifetime 𝑋 of a testing unit follows two-parameter NHD(𝛽, 𝜆), then the probability density function (PDF) 𝑓(⋅) and cumulative distribution function (CDF) 𝐹(⋅), survival function (SF) 𝑆(⋅) and hazard rate function (HRF) 𝐻(⋅), for given mission time 𝑡, are given respectively by…”

Section: Introductionmentioning

confidence: 99%

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Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Elshahhat

Azm

2022

Quality & Reliability Eng

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Generalized progressive hybrid censoring plan proposed to overcome the limitation of the progressive hybrid censoring scheme is that it cannot be applied when very few failures may occur before pre-specified terminal time 𝑇. In this paper, the estimating problems of the model parameters, reliability and hazard rate functions of Nadarajah-Haghighi distribution when a sample is available from generalized progressive hybrid censoring have been considered. The maximum likelihood and Bayes estimators have been obtained for any function of the model parameters. Approximate confidence intervals for the unknown parameters and any function of them are constructed. Using independent gamma informative priors, the Bayes estimators of the unknown parameters are derived under the squared-error loss function. Two approximation techniques, namely: Lindley approximation method and Metropolis Hastings algorithm have been used to carry out the Bayes estimates and also to construct the associate highest posterior density credible intervals. The performance of the proposed methods are evaluated through a Monte Carlo simulation study. To select the optimum censoring scheme among different competing censoring plans, different optimality criteria have been considered. A real-life dataset, representing the failure times of electronic devices, is analyzed to demonstrate how the applicability of the proposed methodologies in real phenomenon.

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Section: Likelihood Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Elshahhat

Azm

2022

Quality & Reliability Eng

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“…In recent years, GPHCS has gained a lot of attention by the researchers. For example one can see, Górny and Cramer (2016), Kotb (2018), Elshahhat (2017) and Maswadah (2021).…”

Section: Case I: {Xmentioning

confidence: 99%

Reliability analysis of K-out-of-N system for Weibull components based on generalised progressive hybrid censored data

Dutta¹,

Kayal²

2022

Preprint

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In this paper, we have investigated the reliability of a K-out-of-N system for the components following Weibull distribution based on the generalized progressive hybrid censored data. We have obtained the maximum likelihood estimates (MLEs) of the unknown parameters and the reliability function of the system. Using asymptotic normality property of MLEs, the corresponding asymptotic confidence intervals are constructed. Furthermore, Bayes estimates are derived under squared error loss function with informative prior by using Markov Chain Monte Carlo (MCMC) technique. Highest posterior density (HPD) credible intervals are obtained. A Monte Carlo simulation study is carried out to compare performance of the established estimates. Finally, a real data set is considered for illustrative purposes.

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“…Koley and Kundu (2017) analysed generalized progressively censored data in the presence of competing risk. Kotb (2018) gave Bayesian prediction bounds for exponential type distribution under generalized progressively hybrid censored (GPH) scenario. Wang (2018) and Wang (2020) considered competing risk Weibull failure times model under GPH for complete and under partially observed failure causes respectively.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Nakagami Distribution under Generalized Type-II Progressive Hybrid Censoring Scheme

Pandey¹,

Ali²

2021

JSR

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The paper presents the two parameter Nakagami distribution as an efficient alternative lifetime model for generalized type-II progressive hybrid censored data. Such a censoring mechanism is most appropriate for recording failure times of robust items which have long lifetimes. Maximum likelihood and Bayes estimates are developed for the unknown parameters including the case of both parameters unknown. Posterior analysis is conducted under squared error and Linear exponential loss functions. A simulation study and two real data based empirical assessment of the theoretic findings of the paper are undertaken as illustrations.

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Bayesian Prediction Bounds for the Exponential-type Distribution Based on Generalized Progressive Hybrid Censoring Scheme

Cited by 5 publications

References 18 publications

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Reliability analysis of K-out-of-N system for Weibull components based on generalised progressive hybrid censored data

Bayesian Analysis of Nakagami Distribution under Generalized Type-II Progressive Hybrid Censoring Scheme

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