Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud

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.

References in corpus (5)

• HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973--2011
• Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud
• Estimating the limiting shape of bivariate scaled sample clouds: with additional benefits of self-consistent inference for existing extremal dependence properties
• Modelling multivariate extremes through angular-radial decomposition of the density function
• Statistical inference for radial generalized Pareto distributions and return sets in geometric extremes

Cited by in corpus (4)

• Generative Machine Learning for Multivariate Angular Simulation
• Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud
• Deep learning joint extremes of metocean variables using the SPAR model
• Estimating the limiting shape of bivariate scaled sample clouds: with additional benefits of self-consistent inference for existing extremal dependence properties