One-sample Bayesian prediction intervals based on progressively type-II censored data from the half-logistic distribution under progressive stress model

AL-Hussaini, Essam K.; Abdel-Hamid, Alaa H.; Hashem, Atef F.

doi:10.1007/s00184-014-0526-4

Cited by 26 publications

(18 citation statements)

References 29 publications

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“…The data obtained from the joint progressive type-II censoring scheme with samples from two production lines are studied by Reference [9]. Reference [10] investigated Bayesian interval prediction under progressive stress accelerated life test based on progressive type-II censored data.…”

Section: Progressive Type-ii Censored Data With Binomial Removalsmentioning

confidence: 99%

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Nie

Gui

2019

Mathematics

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The competing risk model based on Lindley distribution is discussed under the progressive type-II censored sample data with binomial removals. The maximum likelihood estimation of the unknown parameters of the distribution is established. Using the Lindley approximation method, we also obtain the Bayesian estimation of the unknown parameters of the distribution under different loss functions. The performance of different estimates is studied in this article. A real practical dataset is analyzed for illustration.

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Section: Progressive Type-ii Censored Data With Binomial Removalsmentioning

confidence: 99%

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Nie

Gui

2019

Mathematics

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“…Commonly used methods are constant-stress, step-stress, and progressive-stress, see, for example, Nelson,12 AL-Hussaini and Abdel-Hamid, 13,14 and Abdel-Hamid and AL-Hussaini. 15 The stress applied to a test product increases in time during a progressive-stress ALT, see, for example, Yin and Sheng, 16 Abdel-Hamid and AL-Hussaini, 17 Abdel-Hamid and Abushal, 18 and AL-Hussaini et al 19 Censoring is very common in life tests. It may be applied when the experimenter is unable to get total information on lifetimes for each unit or to reduce the total test time and the associated cost.…”

Section: Andmentioning

confidence: 99%

“…where Ω j (t) and (c j , d) are given, respectively, by (18) and (19). HRF (21) of GPRD under progressive-stress ALT is plotted in Figure 5 for different values of a and b.…”

Section: Gprd Under Progressive-stress Modelmentioning

confidence: 99%

Inference for a geometric‐poisson‐Rayleigh distribution under progressive‐stress accelerated life tests based on type‐I progressive hybrid censoring with binomial removals

Nadarajah

Abdel-Hamid

Hashem

2018

Quality & Reliability Eng

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Based on failures of a parallel‐series system, a new distribution called geometric‐Poisson‐Rayleigh distribution is proposed. Some properties of the distribution are discussed. A real data set is used to compare the new distribution with other 6 distributions. The progressive‐stress accelerated life tests are considered when the lifetime of an item under use condition is assumed to follow the geometric‐Poisson‐Rayleigh distribution. It is assumed that the scale parameter of the geometric‐Poisson‐Rayleigh distribution satisfies the inverse power law such that the stress is a nonlinear increasing function of time and the cumulative exposure model for the effect of changing stress holds. Based on type‐I progressive hybrid censoring with binomial removals, the maximum likelihood and Bayes (using linear‐exponential and general entropy loss functions) estimation methods are considered to estimate the involved parameters. Some point predictors such as the maximum likelihood, conditional median, best unbiased, and Bayes point predictors for future order statistics are obtained. The Bayes estimates are obtained using Markov chain Monte Carlo algorithm. Finally, a simulation study is performed, and numerical computations are performed to compare the performance of the implemented methods of estimation and prediction.

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“…Abdel-Hamid and AL-Hussaini 19 applied the progressive stress ALT under progressive censoring for Weibull distribution. AL-Hussaini et al 20 derived Bayesian prediction intervals based on progressively type-II censored data from the half-logistic distribution under progressive-stress model. Abdel-Hamid and Abushul 21 obtained the Bayes estimates (BEs) of exponentiated exponential distribution under type-II progressive hybrid censoring, assuming the inverse power law and the cumulative exposure model.…”

Section: Introductionmentioning

confidence: 99%

Classical and Bayesian inference on progressive‐stress accelerated life testing for the extension of the exponential distribution under progressive type‐II censoring

El-Din

Abu-Youssef

Ali

et al. 2017

Quality & Reliability Eng

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In this paper, a progressive‐stress accelerated life test under progressive type‐II censoring is considered. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. The maximum likelihood and Bayes estimates of the model parameters are obtained. The approximate and credible confidence intervals of the estimators are derived. Furthermore, a real lifetime data set is analyzed to illustrate the proposed procedures. Finally, the simulation studies are used to compare between 2 different designs of the progressive‐stress test (simple and multiple ramp‐stress tests).

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One-sample Bayesian prediction intervals based on progressively type-II censored data from the half-logistic distribution under progressive stress model

Cited by 26 publications

References 29 publications

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Inference for a geometric‐poisson‐Rayleigh distribution under progressive‐stress accelerated life tests based on type‐I progressive hybrid censoring with binomial removals

Classical and Bayesian inference on progressive‐stress accelerated life testing for the extension of the exponential distribution under progressive type‐II censoring

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