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

Moshira Ismail, Sanaa Ismail, Sherouk Samir Moawad

Abstract


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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DOI: http://dx.doi.org/10.18187/pjsor.v15i3.2490

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Title

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

Keywords


Description

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.


Date

2019-09-13

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 15 No. 3, 2019



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810