Bayesian inference for the parameters of mortality rate in the models of dependent lives with application in life insurance

Shirin Shoaee

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 3, 995-1023

Abstract: In this paper, the Bayesian inference of the model of dependent lives is considered. We use the bivariate Gompertz (BGP) distribution. This new bivariate survival model and its extension have recently been proposed by Shoaee and Khorram. This model is more flexible and can be applied in the actuarial science of life insurance, and reliability theory in competing risks and shock models. As we know, the maximum likelihood estimates do not always exist and therefore cannot always be calculated. So, the Bayesian estimations are considered using the squared error loss function and a priori distributions that create a dependency between the hyper-parameters. But given the assumptions, one can see that explicit expressions cannot be obtained for Bayesian estimations. The importance sampling method is proposed to calculate the Bayes estimations and also to create the corresponding HPD credible intervals of the unknown parameters. The Monte Carlo simulation studies and analysis of a real data set are also performed to evaluate the proposed method for estimating parameters. Finally, a generalization of the dependent lives models is presented and the Bayesian estimate for this new bivariate family distribution is introduced and a comparison is made between the models of this new family.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2022.2099719 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:53:y:2024:i:3:p:995-1023

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2022.2099719

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().