• Corpus ID: 251953444

Statistical inference for multivariate extremes via a geometric approach

  title={Statistical inference for multivariate extremes via a geometric approach},
  author={Jennifer L. Wadsworth and Ryan Campbell},
A geometric representation for multivariate extremes, based on the shapes of scaled sample clouds in light-tailed margins and their so-called limit sets, has recently been shown to connect several existing extremal dependence concepts. However, these results are purely probabilistic, and the geometric approach itself has not been fully exploited for statistical inference. We outline a method for parametric estimation of the limit set shape, which includes a useful non/semiparametric estimate as… 



Models and inference for uncertainty in extremal dependence

Conventionally, modelling of multivariate extremes has been based on the class of multivariate extreme value distributions. More recently, other classes have been developed, allowing for the

Estimating the limiting shape of bivariate scaled sample clouds for self-consistent inference of extremal dependence properties

An integral part of carrying out statistical analysis for bivariate extreme events is characterising the tail dependence relationship between the two variables. For instance, we may be interested in

A conditional approach for multivariate extreme values (with discussion)

Summary.  Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d‐dimensional random variable. Existing

Linking representations for multivariate extremes via a limit set

Abstract The study of multivariate extremes is dominated by multivariate regular variation, although it is well known that this approach does not provide adequate distinction between random vectors

A new representation for multivariate tail probabilities

Existing theory for multivariate extreme values focuses upon characterizations of the distributional tails when all components of a random vector, standardized to identical margins, grow at the same

Meta densities and the shape of their sample clouds

Modelling multivariate extreme value distributions

SUMMARY Multivariate extreme value distributions arise as the limiting joint distribution of normalized componentwise maxima/minima. No parametric family exists for the depen- dence between the

Fast Calibrated Additive Quantile Regression

Abstract We propose a novel framework for fitting additive quantile regression models, which provides well-calibrated inference about the conditional quantiles and fast automatic estimation of the

Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions

A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection.

The multivariate Gaussian tail model: an application to oceanographic data

Optimal design of sea‐walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies