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Statistical inference of the exponentiated exponential distribution based on progressive type-II censoring with optimal scheme

Author

Listed:
  • Naresh Chandra Kabdwal

    (Banasthali Vidyapith)

  • Qazi J. Azhad

    (Shiv Nadar Institution of Eminence)

  • Rashi Hora

    (GL Bajaj Institute of Management and Research)

Abstract

This article is concerned with the estimation of parameters, reliability and hazard rate functions of the exponentiated exponential distribution under progressive type-II censoring data. The maximum likelihood estimation and maximum product of spacing methods are presented to estimate the unknown parameters of the model in classical theme. In the Bayesian paradigm, we have considered both likelihood as well as product of spacing functions to estimates of the model parameters, reliability and hazard rate functions. Bayes estimates are considered under squared error loss function (SELF) using gamma prior for the shape parameter and a discrete prior for the scale parameter. Asymptotic confidence and highest posterior density credible intervals have also been obtained for the model parameters and reliability characteristics. Optimal criteria is also employed to find the best censoring scheme among the considered censoring schemes. A Monte Carlo simulation study is used to compare the performances the derived estimators under different progressive type-II censoring schemes. Finally, to illustrate the practical application of the proposed methodology, two real data analysis are conducted.

Suggested Citation

  • Naresh Chandra Kabdwal & Qazi J. Azhad & Rashi Hora, 2024. "Statistical inference of the exponentiated exponential distribution based on progressive type-II censoring with optimal scheme," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3833-3853, August.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02381-0
    DOI: 10.1007/s13198-024-02381-0
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    References listed on IDEAS

    as
    1. Sanku Dey & Ahmed Elshahhat & Mazen Nassar, 2023. "Analysis of progressive type-II censored gamma distribution," Computational Statistics, Springer, vol. 38(1), pages 481-508, March.
    2. Kundu, Debasis & Gupta, Rameshwar D., 2008. "Generalized exponential distribution: Bayesian estimations," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1873-1883, January.
    3. Anurag Pathak & Manoj Kumar & Sanjay Kumar Singh & Umesh Singh, 2022. "Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data," Annals of Data Science, Springer, vol. 9(1), pages 101-119, February.
    4. Biswabrata Pradhan & Debasis Kundu, 2009. "On progressively censored generalized exponential distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 497-515, November.
    5. Gupta, Rameshwar D. & Kundu, Debasis, 2003. "Discriminating between Weibull and generalized exponential distributions," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 179-196, June.
    6. Anatolyev, Stanislav & Kosenok, Grigory, 2005. "An Alternative To Maximum Likelihood Based On Spacings," Econometric Theory, Cambridge University Press, vol. 21(2), pages 472-476, April.
    7. Olayan Albalawi & Naresh Chandra Kabdwal & Qazi J. Azhad & Rashi Hora & Basim S. O. Alsaedi, 2022. "Estimation of the Generalized Logarithmic Transformation Exponential Distribution under Progressively Type-II Censored Data with Application to the COVID-19 Mortality Rates," Mathematics, MDPI, vol. 10(7), pages 1-19, March.
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