Bayesian inferences given a type-<sup>2</sup>censored sample from a burr distribution

Evans, I. G.; Ragab, Aisha

doi:10.1080/03610928308828551

Cited by 40 publications

(13 citation statements)

References 3 publications

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“…It was proposed as a life time model and its properties were studied by Burr and Cislak [24], Dubey [25,26], Tadikamalla [59] and Lewis [42], among others. Inferences, based on the Burr XII (β,) distribution and some of its testing measures were made by Papadopoulos [53], Evans and Ragab [31], Lingappaiah [45], Shah and Gekhale [55], AL-Hussaini and Jaheen [12,13]. Khan and Khan [39] and AL-Hussaini [3] characterized the Burr XII (β,) distribution.…”

Section: Application To Exponentiated Burr Type XII Populationmentioning

confidence: 99%

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

AL-Hussaini¹,

Hussein²

2011

OJS

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Bayesian predictive probability density function is obtained when the underlying pop-ulation distribution is exponentiated and subjective prior is used. The corresponding predictive survival function is then obtained and used in constructing 100(1 – ?)% predictive interval, using one- and two- sample schemes when the size of the future sample is fixed and random. In the random case, the size of the future sample is assumed to follow the truncated Poisson distribution with parameter λ. Special attention is paid to the exponentiated Burr type XII population, from which the data are drawn. Two illustrative examples are given, one of which uses simulated data and the other uses data that represent the breaking strength of 64 single carbon fibers of length 10, found in Lawless [40]

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Section: Application To Exponentiated Burr Type XII Populationmentioning

confidence: 99%

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

AL-Hussaini¹,

Hussein²

2011

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“…Many authors have discussed the problem of estimation of the unknown parameters, while quite a few discussed the prediction problem. For instance, Evans & Ragab (1983) discussed the prediction problem for the life times of the unused items and Nigm (1988) discussed the same problem for the future jth order statistic. They used fixed sample size for the future sample, a discrete prior for the shape parameter and a gamma prior for k. The choice of this prior is not reasonable to express our belief about c. This discrete prior is not a realistic choice.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Prediction of the Median of the Burr Distribution with Fixed and Random Sample Sizes

Ashour

El-Wakeel

1994

Statistics

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Lingappaiah (1986) was the first to introduce the idea of Bayesian prediction in life testing when the size of the future sample is a random variable. In this paper we discuss the Bayesion prediction of the sample median when the parent distribution is a generalized Burr distribution (GBD), the old sample is censored type I1 and the size of the future sample is a random variable. A numerical illustration is provided.

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“…Evans and Simon (1975) discussed the Burr distribution as a failure model and derived the Maximum Likelihood Estimators (MLE) for its parameters. Evans and Ragab (1983) discussed the Bayes estimotors for Burr distribution paramaeters based on type-II censored samples. Wu and Yu (2005) proposed pivotal quantities to test the shape parameter and established its confidence interval based on censored data.…”

Section: Introductionmentioning

confidence: 99%

On the Product and Ratio of Two Generalized Order Statistics From the Generalized Burr Type-Ii Distribution

Maswadah¹

2013

Journal of Mathematics and Statistics

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In this study, the probability density functions of the product and quotient of two generalized order statistics from the generalized Burr Type-XII distribution have been derived, using the Mellin transform and its inverse. Several interesting special cases have also been obtained such as the product and quotient of extreme generalized order statistics and the product and quotient of consecutive generalized order statistics

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Bayesian inferences given a type-²censored sample from a burr distribution

Cited by 40 publications

References 3 publications

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayesian Prediction of the Median of the Burr Distribution with Fixed and Random Sample Sizes

On the Product and Ratio of Two Generalized Order Statistics From the Generalized Burr Type-Ii Distribution

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Bayesian inferences given a type-2censored sample from a burr distribution

Cited by 40 publications

References 3 publications

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayesian Prediction of the Median of the Burr Distribution with Fixed and Random Sample Sizes

On the Product and Ratio of Two Generalized Order Statistics From the Generalized Burr Type-Ii Distribution

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Bayesian inferences given a type-²censored sample from a burr distribution