Abstract
Based on a one-sample scheme, general Bayesian prediction intervals (BPI) for future generalized-order statistics are obtained when the previous and future samples are assumed to follow a general class of continuous distributions. The prior belief of the experimenter is measured by two distributions according to whether one (two) parameter(s) is (are) unknown. BPI for upper-order statistics and upper record values are obtained as special cases. Doubly Type II censored of the observed data has been used here. Application to the Weibull (θ1, θ2) model is illustrated when θ1 is an unknown parameter and when both θ1 and θ2 are unknown parameters. Numerical computations are made when θ1 is unknown to illustrate the procedures.
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