Joint Type-I Censoring in Mixture Weibull Models: Estimation via Maximum Likelihood, Bayesian Methods and Application in Environmental Data

Authors

  • Waleed M. Afify Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
  • Eslam Abdelhakim Seyam Department of Insurance and Risk Management, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
  • Jehan A. Elbanna Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
  • Said G. Nassr Department of Statistics and Insurance, Faculty of Commerce, Arish University, El-Arish 45511, Egypt
  • Neema M. Elharoun Department of Statistics and Insurance, Faculty of Commerce, Arish University, El-Arish 45511, Egypt
  • Ahmed Ramzy Shehata Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Kafrelsheikh University, Kafrelsheikh 33511, Egypt

DOI:

https://doi.org/10.19139/soic-2310-5070-3234

Keywords:

Joint Type-I censored scheme, Mixture Weibull distribution, Maximum likelihood estimation, Bayesian estimators and Markov Chain Monte Carlo

Abstract

In this study, we propose a comprehensive statistical approach to the estimation of the unknown parameters of a mixture of Weibull distribution made of two components using joint Type-I censored samples. Bayesian estimation and Maximum Likelihood Estimation (MLE) methods were considered, with a focus on SE and LINEX loss functions. We utilized the MCMC methods, particularly Metropolis-Hastings algorithm, in order to approximate posterior distribution. Asymptotic confidence intervals and Highest Posterior Density (HPD) intervals were constructed to assess estimation accuracy. Furthermore, we carried out the study of simulation to assess the behavior of the suggested estimators, and the methodology was applied to a real dataset to highlight illustrations.

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Published

2026-10-02

How to Cite

Waleed M. Afify, Seyam, E. A., Jehan A. Elbanna, Nassr, S. G., Elharoun, N. M., & Ahmed Ramzy Shehata. (2026). Joint Type-I Censoring in Mixture Weibull Models: Estimation via Maximum Likelihood, Bayesian Methods and Application in Environmental Data. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3234

Issue

Online First

Section

Research Articles

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Copyright (c) 2026 Waleed M. Afify , Eslam Abdelhakim Seyam, Jehan A. Elbanna, Said G. Nassr, Neema M. Elharoun, Ahmed Ramzy Shehata

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