Progressive Type-II Censoring Schemes of Extended Odd Weibull Exponential Distribution with Applications in Medicine and Engineering

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.

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“…In [35][36][37]. We suggest studying a more flexible censored sample for the proposed distribution in future studies, such as [38,39]. We suggest researching multivariate analysis, such as [40,41] for the proposed distribution.…”

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“…, r, where in Type-I censoring ℘ � T and in Type-II censoring ℘ � x r: n . For more information, see the work of Almetwally and Almongy [27,28] and Alshenawy et al [29,30]. e natural logarithm of the product spacing function in the general form for the two different types of censored samples is given by…”

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“…In alternative estimation methods, the maximum product spacing approach is used to estimate the continuous univariate model parameters as an alternative to the Maximum Likelihood method developed for complete sample by Cheng and Amin [ 23 ] and this developed to use under censored sample by Singh et al [ 24 ], Basu et al [ 25 ], Almetwally et al [ 26 ], El-Sherpieny et al [ 27 ], Alshenawy et al [ 28 , 29 ]. The least-square and weighted least-square methods are used to estimate the parameters of the beta distribution by Swain et al [ 30 ].…”