Composite models with underlying folded distributions
Document Type
Article
Publication Date
7-1-2021
Abstract
In this note, we examine the performance of 25 new composite models that are derived from 5 underlying folded distributions for modeling insurance loss data. These models are assessed using standard selection criteria involving the Akaike Information Criteria and the Bayesian Information Criteria as well as proximity to empirical risk estimates. Three models are found significant in improving the goodness-of-fit than the latest development in the literature with two models reliable for risk estimation. (C) 2020 Elsevier B.V. All rights reserved.
Keywords
Danish fire loss data, Information criteria, Risk estimation
Divisions
Science
Funders
Ministry of Higher Education, Malaysia under Fundamental Research Grant Scheme (FRGS) [FP040-2017A]
Publication Title
Journal of Computational and Applied Mathematics
Volume
390
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS