Journal article

Application of bivariate negative binomial regression model in analysing insurance count data

Feng Liu, David Pitt

Annals of Actuarial Science | Cambridge University Press | Published : 2017

Abstract

In this paper we analyse insurance claim frequency data using the bivariate negative binomial regression (BNBR) model. We use general insurance data on claims from simple third-party liability insurance and comprehensive insurance. We find that bivariate regression, with its capacity for modelling correlation between the two observed claim counts, provides both a superior fit and out-of-sample prediction compared with the more common practice of fitting univariate negative binomial regression models separately to each claim type. Noting the complexity of BNBR models and their potential for a large number of parameters, we explore the use of model shrinkage methodology, namely the least absol..

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University of Melbourne Researchers