Inference of the exponentiated rayleigh distribution on step-stress accelerated life testing under type-i hybrid censored data with physical application

Mohamad A. Fawzy; Ibtesam A. Alasbahi; Tahani A. Aloaf; Abd El Baset A. Ahmad; Hala H. Taha; Alaa A. El Bary; Khaled Lotfy

Ayuda

Inference of the exponentiated rayleigh distribution on step-stress accelerated life testing under type-i hybrid censored data with physical application

Mohamad A. Fawzy ^[1] ; Ibtesam A. Alasbahi ^[2] ; Tahani A. Aloaf ^[3] ; Abd El-Baset A. Ahmad ^[4] ; Hala H. Taha ^[5] ; Alaa A. El-Bary ^[7] ; Khaled Lotfy ^[6]
1. [1] Taibah University
  
  Taibah University
  
  Arabia Saudí
2. [2] Taiz University
  
  Taiz University
  
  Yemen
3. [3] Taif University
  
  Taif University
  
  Arabia Saudí
4. [4] Assiut University
  
  Assiut University
  
  Egipto
5. [5] Princess Nourah bint Abdulrahman University
  
  Princess Nourah bint Abdulrahman University
  
  Arabia Saudí
6. [6] Zagazig University
  
  Zagazig University
  
  Egipto
7. [7] Arab Academy for Science, Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt
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Localización: Métodos numéricos para cálculo y diseño en ingeniería: Revista internacional, ISSN 0213-1315, Vol. 41, Nº 4, 2025
Idioma: inglés
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Resumen
- This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model. The maximum likelihood and Bayes methods estimate the parameters and acceleration factor. The parameters’ approximate confidence intervals are created. The Bayes estimates of the parameters for the squared error and linear exponential loss functions are computed using the Markov Chain Monte Carlo (MCMC) method. Finally, we perform a simulation study to evaluate the effectiveness of the proposed estimators. We provide a real-life data example (Strength data measurement in GPA, for single and impregnated carbon fibers) to explain the obtained results.