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Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...
Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, infere ...
The modeling of multivariate extremes has received increasing recent attention because of its importance in risk assessment. In classical statistics of extremes, the joint distribution of two or more extremes has a nonparametric form, subject to moment con ...
Frequentist and Bayesian approaches to statistics have long been seen as incompatible, but recent work has been done to try and unify them (Bayarri and Berger, 2004; Efron, 2005). Empirical Bayes, approximate Bayesian analysis, and the matching prior appro ...
A new gyrotron simulation code for simulating the beam-wave interaction using a monomode time-dependent self-consistent model is presented. The new code TWANG-PIC is derived from the trajectory-based code TWANG by describing the electron motion in a gyro-a ...
Heavy-tail phenomena are common in real-life data; the finance and insurance industries, telecommunications, and environment-related events offer typical examples of such phenomena. We focus on the particular topic of the extremogram, for which Davis and M ...
We show that, in a restricted range, the divisor function of integers in residue classes modulo a prime follows a Gaussian distribution, and a similar result for Hecke eigenvalues of classical holomorphic cusp forms. Furthermore, we obtain the joint distri ...
We study stationary max-stable processes {n(t): t is an element of R} admitting a representation of the form n(t) = max(i is an element of N) (U-i +Y-i(t)), where Sigma(infinity)(i=1) delta U-i is a Poisson point process on R with intensity e(-u)du, and Y1 ...
Linear regression summarises the link between a variable of interest and one or several explanatory variables through a parametric relationship. Hastie and Tibshirani (1986) extended this approach by allowing a non-parametric description of the dependence ...
