On Bayesian interval prediction of future generalized-order statistics using doubly censoring

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 (theta(1), theta(2)) model is illustrated when theta(1) is an unknown parameter and when both theta(1) and theta(2) are unknown parameters. Numerical computations are made when theta(1) is unknown to illustrate the procedures.