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...

Progressive hybrid censoring scheme is now quite common in the experiment of life testing and reliability analysis. In this article the data, failure life times of units, is obtained by using type-I progressive hybrid censoring scheme. It is assumed that data follows Burr Type XII distribution. The point and interval estimation of the Burr Type XII distribution parameters and acceleration facto...

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 ...

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...

The present article aims to point and interval estimation of the parameters of generalised exponential distribution (GED) under progressive interval type-I (PITI) censoring scheme with random removals. The considered censoring scheme is most useful in those cases where continuous examination is not possible. Maximum likelihood, expectationmaximization and Bayesian procedures have been developed...

Censoring is very common in life tests in the past several decades; the experimenter may be unable to obtain complete information on failure times of all experimental items. For this reason, Aggarwalla [1] suggested a useful type of censoring, namely, a progressively Type I interval censored data, which is a union of Type I interval and progressive censoring. This method of lifetime data collec...

This paper considers parameter estimations in Lomax distribution under progressive type-II censoring with random removals, assuming that the number of units removed at each failure time has a binomial distribution. The maximum likelihood estimators (MLEs) are derived using the expectation-maximization (EM) algorithm. The Bayes estimates of the parameters are obtained using both the squared erro...