A Pareto scale-inflated outlier model and its Bayesian analysis

TL;DR

This paper introduces a Bayesian Pareto scale-inflated outlier model to better handle contaminated data with outliers, and demonstrates its application through three real-world examples, including insurance claims.

Contribution

It presents a novel Pareto outlier model with Bayesian analysis and Gibbs sampling implementation for improved outlier detection in Pareto-distributed data.

Findings

Effective outlier detection in Pareto data

Successful application to insurance claims data

Gibbs sampler implementation for Bayesian inference

Abstract

This paper develops a Pareto scale-inflated outlier model. This model is intended for use when data from some standard Pareto distribution of interest is suspected to have been contaminated with a relatively small number of outliers from a Pareto distribution with the same shape parameter but with an inflated scale parameter. The Bayesian analysis of this Pareto scale-inflated outlier model is considered and its implementation using the Gibbs sampler is discussed. The paper contains three worked illustrative examples, two of which feature actual insurance claims data.