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Statistical inference for modified Weibull distribution based on progressively type-II censored data

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  • Kotb, M.S.
  • Raqab, M.Z.

Abstract

In the context of survival and medical studies, it sounds more natural to have situations where the removal of units prior to failure is preplanned for cost or money constraints. Here in this paper, we consider the inference problem including estimation and prediction for three-parameter modified Weibull distribution based on progressively type-II censored sample data. The maximum likelihood and Bayes approaches based on conjugate and discrete priors for estimating the model parameters are derived. These Bayes estimators are developed and computed using the balanced square error and balanced LINEX loss functions. Approximate confidence intervals and credible intervals of the model parameters are also performed. The point predictors and credible intervals of unobserved units based on an informative progressive type-II censored data in one-sample and two-sample prediction problems are also developed. Monte Carlo simulations are performed for comparison purposes and one real data set is analyzed for illustrative purposes.

Suggested Citation

  • Kotb, M.S. & Raqab, M.Z., 2019. "Statistical inference for modified Weibull distribution based on progressively type-II censored data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 233-248.
  • Handle: RePEc:eee:matcom:v:162:y:2019:i:c:p:233-248
    DOI: 10.1016/j.matcom.2019.01.015
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    1. Raqab, Mohammad Z. & Asgharzadeh, A. & Valiollahi, R., 2010. "Prediction for Pareto distribution based on progressively Type-II censored samples," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1732-1743, July.
    2. Chansoo Kim & Jinhyouk Jung & Younshik Chung, 2011. "Bayesian estimation for the exponentiated Weibull model under Type II progressive censoring," Statistical Papers, Springer, vol. 52(1), pages 53-70, February.
    3. Balakrishnan, N. & Childs, A. & Chandrasekar, B., 2002. "An efficient computational method for moments of order statistics under progressive censoring," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 359-365, December.
    4. Essam AL-Hussaini & Alaa Abdel-Hamid & Atef Hashem, 2015. "One-sample Bayesian prediction intervals based on progressively type-II censored data from the half-logistic distribution under progressive stress model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(7), pages 771-783, October.
    5. Hal R. Varian, 2000. "Variants in Economic Theory," Books, Edward Elgar Publishing, number 1033.
    6. Soliman, Ahmed A. & Abd Ellah, A.H. & Sultan, K.S., 2006. "Comparison of estimates using record statistics from Weibull model: Bayesian and non-Bayesian approaches," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 2065-2077, December.
    7. Kotb, M.S. & Raqab, M.Z., 2017. "Inference and prediction for modified Weibull distribution based on doubly censored samples," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 132(C), pages 195-207.
    8. M. El-Din & A. Shafay, 2013. "One- and two-sample Bayesian prediction intervals based on progressively Type-II censored data," Statistical Papers, Springer, vol. 54(2), pages 287-307, May.
    9. Basak, Prasanta & Basak, Indrani & Balakrishnan, N., 2009. "Estimation for the three-parameter lognormal distribution based on progressively censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3580-3592, August.
    10. Essam A. Ahmed, 2014. "Bayesian estimation based on progressive Type-II censoring from two-parameter bathtub-shaped lifetime model: an Markov chain Monte Carlo approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 752-768, April.
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    Cited by:

    1. M. S. Kotb & M. Z. Raqab, 2021. "Estimation of reliability for multi-component stress–strength model based on modified Weibull distribution," Statistical Papers, Springer, vol. 62(6), pages 2763-2797, December.
    2. Uoseph Hamdi Salemi & Esmaile Khorram & Yuancheng Si & Saralees Nadarajah, 2020. "Sensitivity analysis of censoring schemes in progressively type-II right censored order statistics," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 163-189, March.
    3. Mohammed S. Kotb & Huda M. Alomari, 2024. "Estimating the entropy of a Rayleigh model under progressive first-failure censoring," Statistical Papers, Springer, vol. 65(5), pages 3135-3154, July.
    4. Saadati Nik, A. & Asgharzadeh, A. & Raqab, Mohammad Z., 2021. "Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 508-530.

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