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LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model

Published online by Cambridge University Press:  18 March 2021

Spark C. Tseung ,
Andrei L. Badescu ,
Tsz Chai Fung  and
X. Sheldon Lin
Spark C. Tseung
Affiliation:
Department of Statistical Sciences, University of Toronto, 100 St George Street, Toronto, ON M5S 3G3, Canada
Andrei L. Badescu
Affiliation:
Department of Statistical Sciences, University of Toronto, 100 St George Street, Toronto, ON M5S 3G3, Canada
Tsz Chai Fung
Affiliation:
RiskLab, Department of Mathematics, ETH Zurich, Zurich 8092, Switzerland, Department of Risk Management and Insurance, Georgia State University, Atlanta, GA 30303, USA
X. Sheldon Lin*
Affiliation:
Department of Statistical Sciences, University of Toronto, 100 St George Street, Toronto, ON M5S 3G3, Canada
*
*Corresponding author. E-mail: sheldon@utstat.utoronto.ca
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Abstract

This paper introduces a new julia package, LRMoE, a statistical software tailor-made for actuarial applications, which allows actuarial researchers and practitioners to model and analyse insurance loss frequencies and severities using the Logit-weighted Reduced Mixture-of-Experts (LRMoE) model. LRMoE offers several new distinctive features which are motivated by various actuarial applications and mostly cannot be achieved using existing packages for mixture models. Key features include a wider coverage on frequency and severity distributions and their zero inflation, the flexibility to vary classes of distributions across components, parameter estimation under data censoring and truncation and a collection of insurance ratemaking and reserving functions. The package also provides several model evaluation and visualisation functions to help users easily analyse the performance of the fitted model and interpret the model in insurance contexts.

Keywords

Multivariate regression analysisCensoring and truncationExpectation conditional maximisation algorithmInsurance ratemaking and reservingjulia

Information

Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 419 - 440
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

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