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Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions
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作者 Abd EL-Baset A. Ahmad Areej M. Al-Zaydi 《Open Journal of Statistics》 2015年第6期585-599,共15页
This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly?type II censored sample. We consider the one sample predict... This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly?type II censored sample. We consider the one sample prediction and two sample prediction techniques. Bayesian prediction intervals for the median of future sample of generalized order statistics having odd and even sizes are obtained. Our results are specialized to ordinary order statistics and ordinary upper record values. A mixture of two Gompertz components model is given as an application. Numerical computations are given to illustrate the procedures. 展开更多
关键词 Generalized Order STATISTICS BAYESIAN Prediction Heterogeneous Population DOUBLY Type II Censored SAMPLES one- and two-sample schemes
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Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size
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作者 Essam K. AL-Hussaini M. Hussein 《Open Journal of Statistics》 2011年第1期24-32,共9页
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 ... 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]. 展开更多
关键词 PREDICTIVE Density And Survival Functions one- And two-sample schemes BAYES PREDICTION Exponentiated Population. Exponentiated BURR Type XII Distribution Data Of Carbon Fibers
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