Block Adaptive Progressive Type-II Censored Sampling for the Inverted Exponentiated Pareto Distribution: Parameter Inference and Reliability Assessment

TL;DR

This paper develops a new block adaptive progressive Type-II censored sampling method for the Inverted Exponentiated Pareto distribution, focusing on parameter estimation and reliability analysis, with theoretical and simulation validation.

Contribution

It introduces a novel censored sampling scheme for the IEP distribution and compares maximum likelihood and pivotal estimation methods for parameter inference.

Findings

Both estimation methods perform well in simulations.

The proposed model effectively fits real data.

Asymptotic confidence intervals are successfully derived.

Abstract

This article explores the estimation of unknown parameters and reliability characteristics under the assumption that the lifetimes of the testing units follow an Inverted Exponentiated Pareto (IEP) distribution. Here, both point and interval estimates are calculated by employing the classical maximum likelihood and a pivotal estimation methods. Also, existence and uniqueness of the maximum likelihood estimates are verified. Further, asymptotic confidence intervals are derived by using the asymptotic normality property of the maximum likelihood estimator. Moreover, generalized confidence intervals are obtained by utilizing the pivotal quantities. Additionally, some mathematical developments of the IEP distribution are discussed based on the concept of order statistics. Furthermore, all the estimations are performed on the basis of the block censoring procedure, where an adaptive progressive Type-II censoring is employed to every block. In this regard, the performances of two estimation methods, namely maximum likelihood estimation and pivotal estimation, is evaluated and compared through a simulation study. Finally, a real data is illustrated to demonstrate the flexibility of the proposed IEP model.