Decision-Making Regarding a Novel Bounded Exponentiated Weibull Mixture Model Is Applied to Certain Observed Data

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DOI: https://doi.org/10.28924/2291-8639-23-2025-35
Published: Feb 3, 2025
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Arafa O. Mustafa, Nhla A. Abdalrahman, Malak S. S. Hussain, Nazzik I. M. Mohammed, Salah H. Alshabhi, Mustafa M. Mohammed, Mona Magzoub, Nidal E. Taha, Awad A. Bakery, Decision-Making Regarding a Novel Bounded Exponentiated Weibull Mixture Model Is Applied to Certain Observed Data, Int. J. Anal. Appl., 23 (2025), 35.

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Arafa O. Mustafa, Nhla A. Abdalrahman, Malak S. S. Hussain, Nazzik I. M. Mohammed, Salah H. Alshabhi, Mustafa M. Mohammed, Mona Magzoub, Nidal E. Taha, Awad A. Bakery

Abstract

The exponentiated Weibull mixture model (EWMM) is the most frequently used probability distribution in the disciplines of reliability engineering and applied linguistics. Exponentiated Weibull distributions, on the other hand, are unbounded. A variety of applications digitalize the monitored data and have bounded service regions. Different types of double truncated Weibull mixture models (BEWMM) are discussed in this article. These include the double truncated exponential mixture model (BEMM), the double truncated Rayleigh mixture model (BRMM), the double truncated Weibull mixture model (BWMM), and the double truncated generalized exponential mixture model (BGEMM). By combining a mixture model and bounded support regions, we can create a model that is extremely scalable and can capture a variety of statistical properties of the results, such as mean behavior, distribution, form, and tail behavior. We propose an alternative method for evaluating the model parameters, which aims to maximize the upper bound on the data log-likelihood function. We evaluate the (BEWMM) execution using simulated and actual data.

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