Next Article in Journal
A Credit-Risk Valuation under the Variance-Gamma Asset Return
Next Article in Special Issue
Risk Aversion, Loss Aversion, and the Demand for Insurance
Previous Article in Journal
Stochastic Modeling of Wind Derivatives in Energy Markets
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
Risks 2018, 6(2), 57; https://doi.org/10.3390/risks6020057

On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

1
Department of Statistics, Miami University, Oxford, OH 45056, USA
2
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Sant Boi de Llobregat, Barcelona 08830, Spain
3
School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
*
Author to whom correspondence should be addressed.
Received: 7 March 2018 / Revised: 24 April 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue New Perspectives in Actuarial Risk Management)
Full-Text   |   PDF [373 KB, uploaded 30 May 2018]   |  

Abstract

We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. View Full-Text
Keywords: generalized linear model; cluster-weighted model; ordered stereotype model; ordinal data generalized linear model; cluster-weighted model; ordered stereotype model; ordinal data
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Miljkovic, T.; Fernández, D. On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio. Risks 2018, 6, 57.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top