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

Essam K. AL-Hussaini, M. Hussein

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DOI: 10.4236/ojs.2011.11004                

Abstract

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].

Keywords

Predictive Density And Survival Functions, One- And Two-Sample Schemes, Bayes Prediction, Exponentiated Population. Exponentiated Burr Type XII Distribution, Data Of Carbon Fibers

Conflicts of Interest

The authors declare no conflicts of interest.

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