On the construction of bayesian prediction limits for the weibull distribution

Tziafetas, George N.

doi:10.1080/02331888708802060

Cited by 7 publications

(1 citation statement)

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“…Several authors undertaken the problem assuming type II censoring. Among others are Dunsmore (1974), Evans and Nigm (1980a), Evans and Nigm (1980b), Tziafetas (1987), Nigm (1988), Nigm (1989), Calabria and Pulcini (1994), Howlader and Hossaing (1995), Jaheen The general model adopted in this paper resembles that used by Abd El-Baset and Al-Zaydi (2015). The finite mixture of k components has density function the takes the form,…”

Section: Introductionmentioning

confidence: 99%

Bayesian Prediction of order statistics based on finite mixture of general class of distributions under Random Censoring

Ismail

Moawad

2019

Pak.j.stat.oper.res.

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This paper focuses on the Bayesian prediction of kth ordered future observations modelled by a two-component mixture of general class of distributions. Samples under consideration are subject to random censoring. A closed form of Bayesian predictive density is obtained under a two-sample scheme. Applications to Weibull and Burr XII components are presented and comparisons with previous results are made. A numerical example is presented for special cases of the exponential and Lomax components to obtain interval prediction of first and last order statistics.

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Section: Introductionmentioning

confidence: 99%

Bayesian Prediction of order statistics based on finite mixture of general class of distributions under Random Censoring

Ismail

Moawad

2019

Pak.j.stat.oper.res.

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Assessment and monitoring in clinical trials when survival curves have distinct shapes: a Bayesian approach with Weibull modelling

Lecoutre

Mabika

Derzko

2002

Statistics in Medicine

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The comparison of Weibull distributions with unequal shape parameters, in the case of right censored survival data obtained from independent samples, is considered within the framework of Bayesian statistical methodology. The procedures are illustrated with the example of a mortality study where a new treatment is compared to a placebo. The posterior distributions about relevant parameters, which may provide support for a conclusion of clinical superiority of the treatment, and the predictive distributions, which may guide decision about early stopping at an interim analysis, are considered for a class of appropriate priors.

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Post-data evaluation of prior assessment

Schäbe¹

1993

Statistical Papers

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On the construction of bayesian prediction limits for the weibull distribution

Cited by 7 publications

References 5 publications

Bayesian Prediction of order statistics based on finite mixture of general class of distributions under Random Censoring

Bayesian Prediction of order statistics based on finite mixture of general class of distributions under Random Censoring

Assessment and monitoring in clinical trials when survival curves have distinct shapes: a Bayesian approach with Weibull modelling

Post-data evaluation of prior assessment

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