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Investigation the generalized extreme value under liner distribution parameters for progressive type-II censoring by using optimization algorithms.
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- Author(s): Attwa, Rasha Abd El-Wahab; Sadk, Shimaa Wasfy; Aljohani, Hassan M.
- Source:
AIMS Mathematics (2473-6988); 2024, Vol. 9 Issue 6, p15276-15302, 27p- Subject Terms:
- Source:
- Additional Information
- Abstract: Several random phenomena have been modeled by using extreme value distributions. Based on progressive type-II censored data with three different distributions (i.e., fixed, discrete uniform, and binomial random removal), the statistical inference of the generalized extreme value distribution under liner normalization (GEVL distribution) parameters is investigated in this study. Since there is no analytical solution, determining the maximum likelihood parameters for the GEVL distribution is considered to be a problem. Standard numerical methods are frequently insufficient for this dilemma, requiring the use of artificial intelligence algorithms to address this difficulty. Here, nonlinear minimization and a genetic algorithm have been used to tackle that problem. In addition, Lindley approximation and Monte Carlo estimation were implemented via Metropolis-Hastings algorithms to carry out the Bayesian point estimation based on both the squared error loss function and LINEX loss functions. Moreover, the highest posterior density intervals were applied. The proposed theoretical inference techniques have been applied in a numerical simulation and a real-life example. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of AIMS Mathematics (2473-6988) is the property of American Institute of Mathematical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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