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

作　　者：Essam K. AL-Hussaini M. Hussein 

机构地区：[1]不详

出　　处：《Open Journal of Statistics》2011年第1期24-32,共9页统计学期刊（英文）

摘　　要：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].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].

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 

分 类 号：R73[医药卫生—肿瘤]