This paper deals with the construct and compute in a Bayesian setting, point and interval predictions based on general progressive type II censored data from generalized Pareto distribution. Prediction bounds for the future observations (two sample prediction) based on this type of censored will be derived. Bayesian predictions are obtained based on a continuous–discrete joint prior for the unknown parameters. Bayesian point prediction under symmetric and asymmetric loss functions is discussed. As application, the total duration time in a life test and the failure time of a k-out-of-m system may be predicted. Finally, a real data set has been analyzed for illustrative purposes.
Digital Object Identifier (DOI)
M. El-Sagheer, Rashad
"Bayesian Prediction Based on General Progressive Censored Data from Generalized Pareto Distribution,"
Journal of Statistics Applications & Probability: Vol. 5:
1, Article 4.
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol5/iss1/4