Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...

Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...

Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...

This note discusses and illustrates the use of Weibull probability paper for complete samples and for the special kind of censoring known as type II censoring. In the latter case the observed lifetimes consist of the r lowest values of the sample, the remaining unobserved lifetimes all being higher. Three blanks for 1 Cycle Log, 2 Cycle Log and 3 Cycle Log scales on the measurement abscissa are...

In this study, we consider a censoring scheme called progressive type-II censoring. Under this scheme, n units are placed on a test at time zero, with m failures to be observed. When the first failure is observed, r1 of the surviving units are randomly selected and removed. At the second observed failure, r2 of the surviving units are randomly selected and removed. This experiment stops at the ...

The maximum likelihood estimators (MLEs) and the moment estimators of a two-parameter Birnbaum–Saunders (BISA) distribution are studied by various authors when data are either complete or subject to Type-I or Type-II censoring. But there is not much research on parameter estimation for the BISA distribution under random censoring. A simple method of modified censored moment estimation is propos...

A method for constructing confidence intervals on parameters of a continuous probability distribution is developed in this paper. The objective is to present a model for an uncertainty represented by parameters of a probability density function.  As an application, confidence intervals for the two parameters of the Weibull distribution along with their joint confidence interval are derived. The...

Abstract. In the model of progressive type II censoring, point and interval estimation as well as relations for single and product moments are considered. Based on two-parameter exponential distributions, maximum likelihood estimators (MLEs), uniformly minimum variance unbiased estimators (UMVUEs) and best linear unbiased estimators (BLUEs) are derived for both location and scale parameters. So...