Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Abushal, Tahani A.; AL-Zaydi, Areej M.

doi:10.5539/ijsp.v1n2p20

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…Also, many authors have focused on the problem of predicting either TSP or OSP and TSP together based on various types of censored data from different lifetime models, see, for example, Mahmoud et al [ 14 ], EL-Sagheer [ 15 ], Ahmed [ 16 ], and Abushal and Al-Zaydi [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model

El-Morshedy

EL‐Sagheer

El-Essawy

et al. 2022

Computational Intelligence and Neuroscience

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New Weibull-Pareto distribution is a significant and practical continuous lifetime distribution, which plays an important role in reliability engineering and analysis of some physical properties of chemical compounds such as polymers and carbon fibres. In this paper, we construct the predictive interval of unobserved units in the same sample (one sample prediction) and the future sample based on the current sample (two-sample prediction). The used samples are generated from new Weibull-Pareto distribution due to a progressive type-II censoring scheme. Bayesian and maximum likelihood approaches are implemented to the prediction problems. In the Bayesian approach, it is not easy to simplify the predictive posterior density function in a closed form, so we use the generated Markov chain Monte Carlo samples from the Metropolis-Hastings technique with Gibbs sampling. Moreover, the predictive interval of future upper-order statistics is reported. Finally, to demonstrate the proposed methodology, both simulated data and real-life data of carbon fibres examples are considered to show the applicabilities of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model

El-Morshedy

EL‐Sagheer

El-Essawy

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Equation(41)in Equation(30) and solving, numerically, Equations(31) and(32) we can obtain the lower and upper bounds of BPI.…”

mentioning

confidence: 99%

Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions

Ahmad¹,

AL-Zaydi²

2015

OJS

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This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly type II censored sample. We consider the one sample prediction and two sample prediction techniques. Bayesian prediction intervals for the median of future sample of generalized order statistics having odd and even sizes are obtained. Our results are specialized to ordinary order statistics and ordinary upper record values. A mixture of two Gompertz components model is given as an application. Numerical computations are given to illustrate the procedures.

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Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Cited by 2 publications

References 38 publications

One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model

One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model

Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions

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