BAYESIAN TWO-SAMPLE PREDICTION OF THE GENERALIZED PARETO DISTRIBUTION WITH FIXED AND RANDOM SAMPLE SIZES BASED ON GENERALIZED ORDER STATISTICS (JOURNAL OF ADVANCES IN MATHEMATICS, 2014)
Bayesian predictive intervals for future observations from a future sample from the generalized Pareto distribution (GPD) based on generalized order statistics (GOS) are obtained when the shape parameter is unknown. We consider two cases: (i) fixed sample size (FSS), and (ii) random sample size (RSS).Some closed forms for the Bayesian predictive functions are obtained. Finally examples are calculated for the lower and the upper bounds of the future observations in cases when the future sample is ordinary order statistics (OOS), record values and progressive type II censoring with different values for the scale parameter