New methods to define heavy-tailed distributions with applications to insurance data

Autor:	Zubair Ahmad, Eisa Mahmoudi, G. G. Hamedani, Omid Kharazmi
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	weibull distribution heavy-tailed distributions characterizations monte carlo simulation actuarial measures medical care insurance data vehicle insurance data bayesian estimation Science (General) Q1-390
Zdroj:	Journal of Taibah University for Science, Vol 14, Iss 1, Pp 359-382 (2020)
Druh dokumentu:	article
ISSN:	1658-3655 16583655
DOI:	10.1080/16583655.2020.1741942
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/c50c59c9176048acae5a250adedd3463 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF