Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud

TL;DR

This paper introduces a Bayesian semiparametric method to estimate the shape of the limiting set in bivariate tail analysis, enabling flexible, interpretable inference on tail dependence using Bezier splines and MCMC.

Contribution

It develops a novel Bayesian approach with shape-constrained priors for modeling the limit set in tail dependence, combining Bezier splines with pseudo-polar coordinates.

Findings

Successfully applied to fire risk and air pollution data.

Provides interpretable inference on tail dependence classes.

Ensures valid limit set shapes through prior constraints.

Abstract

We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit set. Characteristics of the shape of the limit set correspond to key tail dependence properties. We directly model the shape of the limit set using Bezier splines, which allow flexible and parsimonious specification of shapes in two dimensions. We fit the Bezier splines to data in pseudo-polar coordinates using Markov chain Monte Carlo sampling, utilizing a limiting approximation to the conditional likelihood of the radii given angles. We propose a novel prior on the shape of the limit set via constraints on the parameters of the Bezier splines. A direct advantage of our Bayesian approach is that the support of this prior guarantees that each posterior sample is a valid limit set boundary, allowing direct posterior analysis of any quantity derived from the shape of the curve. Furthermore, we obtain interpretable inference on the asymptotic dependence class by using mixture priors with point masses on the corner of the unit box. Finally, we apply our model to bivariate datasets of extremes of variables related to fire risk and air pollution.