Discrimination for Two Way Models with Insurance Application

TL;DR

This paper reviews model selection methods for variance models in insurance, applying reversible jump algorithms and comparing results with Deviance Information Criterion to ensure consistency in workers' compensation data analysis.

Contribution

It introduces the application of reversible jump Markov Chain Monte Carlo for model selection in insurance variance models, comparing it with traditional criteria.

Findings

Reversible jump algorithm provides consistent model selection results.

Comparison shows agreement between reversible jump and Deviance Information Criterion.

Application to real insurance data demonstrates practical utility.

Abstract

In this paper, we review and apply several approaches to model selection for analysis of variance models which are used in a credibility and insurance context. The reversible jump algorithm is employed for model selection, where posterior model probabilities are computed. We then apply this method to insurance data from workers' compensation insurance schemes. The reversible jump results are compared with the Deviance Information Criterion, and are shown to be consistent.