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Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) pp 277–285Cite as

Bivariate Weibull Exponential Model Based on Gaussian Copula

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Abstract

The Weibull distribution is widely used as a life-time distribution in many fields such as reliability engineering and social science. The aim of this paper is to introduce a new bivariate model of Weibull and exponential distributions. The emerging based on Gaussian copula, which is a popular used in various applications like econometrics and finance. We discuss the goodness of fit test for copula and use both parametric and semi-parametric methods to estimate the model parameters. Finally, Simulation is studied to illustrate methods of inference and examine the satisfactory performance of the proposed distribution.

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References

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Author information

Authors and Affiliations

  1. Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia

    Zakiah Ibrahim Kalantan & Mervet Khalifah Abd Elaal

  2. Department of Statistics, Al-Azhar University, Cairo, Egypt

    Mervet Khalifah Abd Elaal

Authors
  1. Zakiah Ibrahim Kalantan
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  2. Mervet Khalifah Abd Elaal
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Corresponding author

Correspondence to Zakiah Ibrahim Kalantan .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA (UiTM) Kedah , Sungai Petani, Kedah, Malaysia

    Liew-Kee Kor

  2. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Abd-Razak Ahmad

  3. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Zanariah Idrus

  4. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Kamarul Ariffin Mansor

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Kalantan, Z.I., Elaal, M.K.A. (2019). Bivariate Weibull Exponential Model Based on Gaussian Copula. In: Kor, LK., Ahmad, AR., Idrus, Z., Mansor, K. (eds) Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017). Springer, Singapore. https://doi.org/10.1007/978-981-13-7279-7_34

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  • DOI: https://doi.org/10.1007/978-981-13-7279-7_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7278-0

  • Online ISBN: 978-981-13-7279-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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