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A Generalized Family of Exponentiated Composite Distributions

Author

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  • Bowen Liu

    (Department of Mathematical Sciences, University of Nevada, Las Vegas, NV 89154, USA
    These authors contributed equally to this work.)

  • Malwane M. A. Ananda

    (Department of Mathematical Sciences, University of Nevada, Las Vegas, NV 89154, USA
    These authors contributed equally to this work.)

Abstract

In this paper, we propose a new family of distributions, by exponentiating the random variables associated with the probability density functions of composite distributions. We also derive some mathematical properties of this new family of distributions, including the moments and the limited moments. Specifically, two special models in this family are discussed. Three real datasets were chosen, to assess the performance of these two special exponentiated-composite models. When fitting to these three datasets, these three special exponentiated-composite distributions demonstrate significantly better performance, compared to the original composite distributions.

Suggested Citation

  • Bowen Liu & Malwane M. A. Ananda, 2022. "A Generalized Family of Exponentiated Composite Distributions," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1895-:d:829889
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    References listed on IDEAS

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    1. Beirlant, J. & Matthys, G. & Dierckx, G., 2001. "Heavy-Tailed Distributions and Rating," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 37-58, May.
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    3. M. S. Aminzadeh & M. Deng, 2019. "Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 1938-1954, April.
    4. Vytaras Brazauskas & Andreas Kleefeld, 2016. "Modeling Severity and Measuring Tail Risk of Norwegian Fire Claims," North American Actuarial Journal, Taylor & Francis Journals, vol. 20(1), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

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