Analyzing Insurance Data with An Exponentiated Composite Inverse Gamma-Pareto Model

Document Type

Article

Publication Date

3-14-2022

Publication Title

Communications in Statistics - Theory and Methods

First page number:

1

Last page number:

14

Abstract

Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets.

Keywords

Composite models; Goodness-of-fit; Inverse gamma distribution; Pareto distribution; Exponentiated models; Insurance data modeling

Disciplines

Applied Mathematics | Numerical Analysis and Computation

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/


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