Multivariate claim count regression model with varying dispersion and dependence parameters

2023/01/12 by Himchan Jeong, George Tzougas, Tsz Chai Fung
Computer Science · Decision Sciences · Mathematics · #Bayesian Methods and Mixture Models #Probability and Risk Models #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.1093/jrsssa/qnac010

openalex publication_date 2023/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Abstract The aim of this paper is to present a regression model for multivariate claim frequency data with dependence structures across the claim count responses, which may be of different sign and range, and overdispersion from the unobserved heterogeneity due to systematic effects in the data. For illustrative purposes, we consider the bivariate Poisson-lognormal regression model with varying dispersion. Maximum likelihood estimation of the model parameters is achieved through a novel Monte Carlo expectation–maximization algorithm, which is shown to have a satisfactory performance when we exemplify our approach to Local Government Property Insurance Fund data from the state of Wisconsin.

Citations

• Bubbles and constraints on debt accumulation
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
• AIC and BIC

Related

• Bayesian CART models for aggregate claim modeling
• Composite Lognormal-T regression models with varying threshold and its insurance application
• Modeling Insurance Claims using Bayesian Nonparametric Regression
• Insurance Applications of Some New Dependence Models derived from\n Multivariate Collective Models
• A comparative analysis of several multivariate zero-inflated and zero-modified models with applications in insurance