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On Modeling the Earthquake Insurance Data via a New Member of the T-X Family
Joint Authors
Ahmad, Zubair
Mahmoudi, Eisa
Kharazmi, Omid
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-19
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
Heavy-tailed distributions play an important role in modeling data in actuarial and financial sciences.
In this article, a new method is suggested to define new distributions suitable for modeling data with a heavy right tail.
The proposed method may be named as the Z-family of distributions.
For illustrative purposes, a special submodel of the proposed family, called the Z-Weibull distribution, is considered in detail to model data with a heavy right tail.
The method of maximum likelihood estimation is adopted to estimate the model parameters.
A brief Monte Carlo simulation study for evaluating the maximum likelihood estimators is done.
Furthermore, some actuarial measures such as value at risk and tail value at risk are calculated.
A simulation study based on these actuarial measures is also done.
An application of the Z-Weibull model to the earthquake insurance data is presented.
Based on the analyses, we observed that the proposed distribution can be used quite effectively in modeling heavy-tailed data in insurance sciences and other related fields.
Finally, Bayesian analysis and performance of Gibbs sampling for the earthquake data have also been carried out.
American Psychological Association (APA)
Ahmad, Zubair& Mahmoudi, Eisa& Kharazmi, Omid. 2020. On Modeling the Earthquake Insurance Data via a New Member of the T-X Family. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1138816
Modern Language Association (MLA)
Ahmad, Zubair…[et al.]. On Modeling the Earthquake Insurance Data via a New Member of the T-X Family. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1138816
American Medical Association (AMA)
Ahmad, Zubair& Mahmoudi, Eisa& Kharazmi, Omid. On Modeling the Earthquake Insurance Data via a New Member of the T-X Family. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1138816
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138816