Abstract: Data depth measures the centrality of a point with respect to a given distribution or data cloud. It provides a natural center-outward ordering of multivariate data points and yields a systematic nonparametric multivariate analysis scheme. In particular, the halfspace depth is shown to...

Let (X1, Y1),…., (Xn, Yn) be an i.i.d. sample from a bivariate distribution function that lies in the max-domain of attraction of<br/>an extreme value distribution. The asymptotic joint distribution of the standardized component-wise maxima<br/>√n i=1 Xi and √n i=1 Yi is then characterized by the...

In this paper we shall be interested in two questions on extremes relating to world records in athletics.The first question is: what is the ultimate world record in a specific athletics event (such as the 100m for men or the high jump for women), given today's state of the art?Our second...

We introduce generalized Probability-Probability (P-P) plots in order to study the one-sample goodness-of-fit problem and the two-sample problem, for real valued data.These plots, that are constructed by indexing with the class of closed intervals, globally preserve the properties of classical...

Consider the extreme quantile region, induced by the halfspace depth function HD, of the form Q = fx 2 Rd : HD(x; P) g, such that PQ = p for a given, very small p 0. This region can hardly be estimated through a fully nonparametric procedure since the sample halfspace depth is 0 outside the...

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the...

AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.

Tail dependence models for distributions attracted to a max-stable law are tted using observations above a high threshold. To cope with spatial, high-dimensional data, a rankbased M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the...

Consider n i.i.d. random vectors on R2, with unknown, common distribution function F.Under a sharpening of the extreme value condition on F, we derive a weighted approximation of the corresponding tail copula process.Then we construct a test to check whether the extreme value condition holds by...

AMS 2000 subject classifications. Primary 62G32, 62G05; secondary 60G70, 60F05.