Abstract
In this paper, the parameters of the extended odd Weibull exponential distribution are estimated under progressive type-II censoring scheme with random removal. The model parameters are estimated using the maximum product spacing and maximum likelihood estimation methods. Further, we explore the asymptotic confidence intervals and bootstrap confidence intervals for the model parameters. Monte Carlo simulations are performed to compare between the proposed estimation methods under progressive type-II censoring scheme. An empirical study using two real datasets form engineering and medicine fields to validate the introduced methods of inference. Based on our study, we can conclude that the maximum product of spacing method outperforms the maximum likelihood method for estimating the extended odd Weibull exponential (EOWE) parameters under a progressive type-II censoring scheme in both numerical and empirical cases.
Highlights
Life-testing and reliability experiments contain many situations where units are removed or lost from the test before failure
We extend the work of Afify and Mohamed [7] by considering the estimation of the extended odd Weibull exponential (EOWE) parameters under progressive type-II censoring scheme with random removal based on the maximum product spacing (MPS) and maximum likelihood estimation methods
; α, β, λ) m:m:n if i = 1, if i = 2, · · ·, m, if i = m, The maximum product of spacing estimators (MPSEs) can be obtained by maximizing the product of spacings which are defined under progressive type-II censoring sample as follows
Summary
Life-testing and reliability experiments contain many situations where units are removed or lost from the test before failure. We extend the work of Afify and Mohamed [7] by considering the estimation of the extended odd Weibull exponential (EOWE) parameters under progressive type-II censoring scheme with random removal based on the maximum product spacing (MPS) and maximum likelihood estimation methods. Due to its flexibility and simple closed forms of its HRF and cumulative distribution function (CDF), we can use it for analyzing censored data They studied the estimation of its parameters using eight classical estimation methods called, the maximum product of spacing estimators, maximum likelihood estimators, least squares estimators, weighted least-squares estimators, percentiles estimators, Cramér-von Mises estimators, Anderson-Darling estimators, and right-tail Anderson-Darling estimators. Et al [14,15] introduced the adaptive type-II progressive censoring schemes using MPS method.
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