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 a random sample in the max-domain of attraction of a multivariate extreme value distribution such that the dependence structure of the attractor belongs to a parametric model. A new estimator for the unknown parameter is defined as the value that minimises the distance between a vector...

AMS 2000 subject classifications: Primary 62G05, 62G30, 62G32; secondary 60G70, 60F05, 60F17, JEL: C13, C14.

AMS 2000 subject classifications: 60G70, 62H12, 62H15, 62F05, 62F12, 62F25.

The existence of data with different dependence structures motivates the development of models which can capture several types of dependence. In this paper we consider a stationary sequence of moving maxima vectors <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\{{\mathbf {X}}_n=(X_{n1},\ldots ,X_{nd})\}_{n\ge 1}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mrow> <mo stretchy="false">{</mo> <msub> <mi mathvariant="bold">X</mi> <mi>n</mi> </msub> <mo>=</mo> <mrow> <mo stretchy="false">(</mo> <msub> <mi>X</mi> <mrow> <mi>n</mi>...</mn></mrow></msub></mrow></mrow></msub></math></equationsource></equationsource></inlineequation>

The probabilistic framework of extreme value theory is well-known: the dependence structure of large events is characterized by an angular measure on the positive orthant of the unit sphere. The family of these angular measures is non-parametric by nature. Nonetheless, any angular measure may be...

The extremal dependence of a random vector describes the tail behaviors of joint probabilities of the random vector with respect to that of its margins, and has been often studied by using the tail dependence function of its copula. A tail density approach is introduced in this paper to analyze...