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Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

Naif Alotaibi1, A. S. Al-Moisheer2, Ibrahim Elbatal1, Mohammed Elgarhy3,4, Ehab M. Almetwally1,5,*

1 Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
2 Department of Mathematics, College of Science, Jouf University, P. O. Box 848, Sakaka, 72351, Saudi Arabia
3 Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt
4 Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, Egypt
5 Faculty of Business Administration, Delta University for Science and Technology, Gamasa, 11152, Egypt

* Corresponding Author: Ehab M. Almetwally. Email: email

(This article belongs to the Special Issue: Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond)

Computer Modeling in Engineering & Sciences 2024, 140(3), 2795-2823. https://doi.org/10.32604/cmes.2024.049188

Abstract

This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of statistical features. The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data. In order to evaluate the effectiveness of these techniques, we provide a set of simulated data for testing purposes. The relevance of the newly presented model is shown via two real-world dataset applications, highlighting its superiority over other respected similar models.

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APA Style
Alotaibi, N., Al-Moisheer, A.S., Elbatal, I., Elgarhy, M., Almetwally, E.M. (2024). Bayesian and non-bayesian analysis for the sine generalized linear exponential model under progressively censored data. Computer Modeling in Engineering & Sciences, 140(3), 2795-2823. https://doi.org/10.32604/cmes.2024.049188
Vancouver Style
Alotaibi N, Al-Moisheer AS, Elbatal I, Elgarhy M, Almetwally EM. Bayesian and non-bayesian analysis for the sine generalized linear exponential model under progressively censored data. Comput Model Eng Sci. 2024;140(3):2795-2823 https://doi.org/10.32604/cmes.2024.049188
IEEE Style
N. Alotaibi, A.S. Al-Moisheer, I. Elbatal, M. Elgarhy, and E.M. Almetwally "Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data," Comput. Model. Eng. Sci., vol. 140, no. 3, pp. 2795-2823. 2024. https://doi.org/10.32604/cmes.2024.049188



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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