Predicting observables from a general class of distributions. (English) Zbl 0933.62018
Summary: A general class of distributions is proposed to be the underlying population model from which observables are to be predicted using the Bayesian approach. This class of distributions includes, among others, the Weibull, compound Weibull (or three-parameter Burr-type XII), Pareto, beta, Gompertz and compound Gompertz distributions. A proper general prior density function is suggested and the predictive density functions are obtained in the one- and two-sample cases. The informative sample is assumed to be a type II censored sample. Illustrative examples of Weibull \((\alpha,\beta)\), Burr-type XII \((\alpha,\beta)\), and Pareto \((\alpha,\beta)\) distributions are given and compared with the results obtained by previous researchers.
Keywords:
Bayes prediction; informative sample; future sample; type II censoring; one- and two-sample schemes; lifetime distributionsReferences:
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