Modelling heavy tail data with bayesian nonparametric mixtures

TL;DR

This paper introduces Bayesian nonparametric mixture models to effectively analyze heavy tail data, capturing both the bulk and tail regions without discarding information, using advanced MCMC techniques.

Contribution

It proposes a novel mixture modeling approach combining shifted gamma-gamma distributions and normalized stable processes for heavy tail data analysis.

Findings

Accurate estimation of heavy tail proportions.

Effective modeling of data's bulk and tail regions.

Demonstrated success on simulated and real datasets.

Abstract

In the study of heavy tail data, several models have been introduced. If the interest is in the tail of the distribution, block maxima or excess over thresholds are the typical approaches, wasting relevant information in the bulk of the data. To avoid this, two building block mixture models for the body (below the threshold) and the tail (above the threshold) are proposed. In this paper, we exploit the richness of nonparametric mixture models to model heavy tail data. We specifically consider mixtures of shifted gamma-gamma distributions with four parameters and a normalised stable processes as a mixing distribution. One of these parameters is associated with the tail. By studying the posterior distribution of the tail parameter, we are able to estimate the proportion of the data that supports a heavy tail component. We develop an efficient MCMC method with adapting Metropolis-Hastings steps to obtain posterior inference and illustrate with simulated and real datasets.