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

Feng Liu, David Pitt

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper we analyse insurance claim frequency data using the bivariate negative binomia 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 absolute shrinkage and selection operator (Lasso) and ridge regression. We find that models estimated using shrinkage methods outperform the ordinary likelihood-based models when being used to make predictions out-of-sample. We find that the Lasso performs better than ridge regression as a method of shrinkage.
Original languageEnglish
Pages (from-to)390-411
Number of pages22
JournalAnnals of Actuarial Science
Issue number2
Publication statusPublished - Sept 2017


  • bivariate negative binomial regression model
  • Lasso
  • ridge regression


Dive into the research topics of 'Application of bivariate negative binomial regression model in analysing insurance count data'. Together they form a unique fingerprint.

Cite this