New Methods to Define Heavy-Tailed Distributions with Applications to Insurance Data

Authors

Gholamhossein Hamedani, Marquette UniversityFollow
Yinglin Liu, Dali University
Muhammad Ilayas, University of Malakand
Saima K. Khosa, Bahauddin Zakariya University
Eisa Muhmoudi, Yazd University
Zubair Ahmad, Yazd UniversityFollow
Dost Muhammad Khan, Abdul Wali UniversityFollow

Document Type

Article

Language

eng

Publication Date

3-2020

Publisher

Elsevier

Source Publication

Journal of Taibah University of Science

Source ISSN

1658-3655

Abstract

Heavy-tailed distributions play an important role in modelling data in actuarial and financial sciences. In this article, nine new methods are suggested to define new distributions suitable for modelling data with an heavy right tail. For illustrative purposes, a special sub-model is considered in detail. Maximum likelihood estimators of the model parameters are obtained and a Monte Carlo simulation study is carried out to assess the behaviour of the estimators. Furthermore, some actuarial measures are calculated. A simulation study based on these actuarial measures is done. The usefulness of the proposed model is proved empirically by means of two real data sets. Finally, Bayesian analysis and performance of Gibbs sampling for the data sets are also carried out.

Comments

Published version. Journal of Taibah University of Science, Vol. 14, No. 1 (March, 2020): 359-382. DOI. © 2020 Elsevier. Used with permission.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Hamedani, Gholamhossein; Liu, Yinglin; Ilayas, Muhammad; Khosa, Saima K.; Muhmoudi, Eisa; Ahmad, Zubair; and Khan, Dost Muhammad, "New Methods to Define Heavy-Tailed Distributions with Applications to Insurance Data" (2020). Mathematical and Statistical Science Faculty Research and Publications. 49.
https://epublications.marquette.edu/math_fac/49