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Bivariate Weibull Distribution: Properties and Different Methods of Estimation

Author

Listed:
  • Ehab Mohamed Almetwally

    (Cairo University)

  • Hiba Zeyada Muhammed

    (Cairo University)

  • El-Sayed A. El-Sherpieny

    (Cairo University)

Abstract

The bivariate Weibull distribution is an important lifetime distribution in survival analysis. In this paper, Farlie–Gumbel–Morgenstern (FGM) copula and Weibull marginal distribution are used for creating bivariate distribution which is called FGM bivariate Weibull (FGMBW) distribution. FGMBW distribution is used for describing bivariate data that have weak correlation between variables in lifetime data. It is a good alternative to bivariate several lifetime distributions for modeling real-valued data in application. Some properties of the FGMBW distribution are obtained such as product moment, skewness, kurtosis, moment generation function, reliability function and hazard function. Three different estimation methods for parameters estimation are discussed for FGMBW distribution namely; maximum likelihood estimation, inference function for margins method and semi-parametric method. To evaluate the performance of the estimators, a Monte Carlo simulations study is conducted to compare the preferences between estimation methods. Also, a real data set is introduced, analyzed to investigate the model and useful results are obtained for illustrative purposes.

Suggested Citation

  • Ehab Mohamed Almetwally & Hiba Zeyada Muhammed & El-Sayed A. El-Sherpieny, 2020. "Bivariate Weibull Distribution: Properties and Different Methods of Estimation," Annals of Data Science, Springer, vol. 7(1), pages 163-193, March.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:1:d:10.1007_s40745-019-00197-5
    DOI: 10.1007/s40745-019-00197-5
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    References listed on IDEAS

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    1. Kundu, Debasis & Gupta, Arjun K., 2013. "Bayes estimation for the Marshall–Olkin bivariate Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 271-281.
    2. Gregor Weiß, 2011. "Copula parameter estimation by maximum-likelihood and minimum-distance estimators: a simulation study," Computational Statistics, Springer, vol. 26(1), pages 31-54, March.
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    5. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
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    7. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
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    Cited by:

    1. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    2. Dina A. Ramadan & Ehab M. Almetwally & Ahlam H. Tolba, 2023. "Statistical Inference to the Parameter of the Akshaya Distribution under Competing Risks Data with Application HIV Infection to AIDS," Annals of Data Science, Springer, vol. 10(6), pages 1499-1525, December.
    3. El-Sayed A. El-Sherpieny & Ehab M. Almetwally & Hiba Z. Muhammed, 2023. "Bayesian and Non-Bayesian Estimation for the Parameter of Bivariate Generalized Rayleigh Distribution Based on Clayton Copula under Progressive Type-II Censoring with Random Removal," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1205-1242, August.
    4. Hanan Haj Ahmad & Ehab M. Almetwally & Dina A. Ramadan, 2023. "Investigating the Relationship between Processor and Memory Reliability in Data Science: A Bivariate Model Approach," Mathematics, MDPI, vol. 11(9), pages 1-23, May.
    5. Mohamed Ibrahim & Khaoula Aidi & M. Masoom Ali & Haitham M. Yousof, 2023. "A Novel Test Statistic for Right Censored Validity under a new Chen extension with Applications in Reliability and Medicine," Annals of Data Science, Springer, vol. 10(5), pages 1285-1299, October.
    6. Hiba Z. Muhammed & Ehab M. Almetwally, 2023. "Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type-II Censoring," Annals of Data Science, Springer, vol. 10(2), pages 481-512, April.
    7. Abdul Ghaniyyu Abubakari & Claudio Chadli Kandza-Tadi & Edwin Moyo, 2023. "Modified Beta Inverse Flexible Weibull Extension Distribution," Annals of Data Science, Springer, vol. 10(3), pages 589-617, June.
    8. Roger Tovar-Falón & Guillermo Martínez-Flórez & Luis Páez-Martínez, 2023. "Bivariate Unit-Weibull Distribution: Properties and Inference," Mathematics, MDPI, vol. 11(17), pages 1-19, September.

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