Abstract
The paper deals with the estimation problem for the generalized Pareto distribution based on progressive type-II censoring with random removals. The number of components removed at each failure time is assumed to follow a binomial distribution. Maximum likelihood estimators and the asymptotic variance-covariance matrix of the estimates are obtained. Finally, a numerical example is given to illustrate the obtained results
Highlights
The paper deals with the estimation problem for the generalized Pareto distribution based on progressive type-II censoring with random removals
The generalized Pareto distribution is known as the Lomax distribution with two parameters, or the Pareto of the second type
This paper is concerned with the estimation problem of the unknown parameters for the generalized Pareto distribution based on progressive type-II censoring with random removals
Summary
The generalized Pareto distribution is known as the Lomax distribution with two parameters, or the Pareto of the second type. The experimenter does not always observe the failure times of all components placed on the test In such cases, the censored sampling arises. There are some cases in which components are lost or removed from the test before failure Shuo [9] studied the estimation problem for two-parameter Pareto distribution based on progressive censoring with uniform removals. Wu et al [11] studied the Burr type XII distribution based on progressively censored samples with random removals. This paper is concerned with the estimation problem of the unknown parameters for the generalized Pareto distribution based on progressive type-II censoring with random removals.
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