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Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
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Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
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Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications

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Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications
Journal Article

Investigating the Lifetime Performance Index under Ishita Distribution Based on Progressive Type II Censored Data with Applications

2023
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Overview
In manufacturing sectors, product performance evaluation is crucial. The lifetime performance index, denoted as CL, is widely used in product evaluation, where L signifies the lower specification limit. This study aims to refine the estimation of CL by employing maximum-likelihood and Bayesian methodologies, where symmetric and asymmetric loss functions are utilized. The analysis is conducted on progressive type II censored data, a requirement often imposed by budgetary constraints or the need for expedited testing. The data are assumed to follow the Ishita distribution, whose conforming rate is also evaluated. Furthermore, a hypothesis testing framework is employed to validate whether component lifetimes meet predefined standards. The theoretical findings are corroborated using real data collected from glass strength in aircraft windows. The numerical analysis emphasizes the goodness of fit of the Ishita distribution to model the data, thereby demonstrating the applicability of the proposed distribution.