@article{ref1,
title="On modeling the earthquake insurance data via a new member of the T-X family",
journal="Computational intelligence and neuroscience",
year="2020",
author="Ahmad, Zubair and Mahmoudi, Eisa and Kharazmi, Omid",
volume="2020",
number="",
pages="e7631495-e7631495",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1687-5265",
doi="10.1155/2020/7631495",
url="http://dx.doi.org/10.1155/2020/7631495"
}