Prediction Intervals based on Doubly Type-II Censored Data from Gompertz Distribution in the Presence of Outliers

The study aims at getting the Bayesian predication intervals for some order statistics of future observations from the distribution of Gompertz (Gomp (a;s)). Doubly Type-II censored data has assisted obtaining in the presence of single outlier that arose from the different same family members of distribution. Single outlier of type s s0 and s+ s0 are considered and bivariate independent prior density for a and s are used. The problem of solving the Double integral to obtain the closed form for a and s, leads us to use MCMC for calculating the Bayesian Predication Intervals. The use of numerical examples and statistical data has enable to properly present and describe the procedure. We conclude that the Bayesian predication intervals are shorter for y1 than y5 when we are increasing the s0 value.