Let {X (t), t >= 0} be a stationary Gaussian process with zero-mean and unit variance. A deep result derived in Piterbarg (2004) [23], which we refer to as Piterbarg's max-discretisation theorem gives the joint asymptotic behaviour (T -> infinity) of the continuous time maximum M(T) = max(t is an element of[0,T]) X(t), and the maximum M-delta(T) = max(t is an element of R(delta)) X(t), with R(delta) subset of [0, T] a uniform grid of points of distance delta = delta(T). Under some asymptotic restrictions on the correlation function Piterbarg's max-discretisation theorem shows that for the limit result it is important to know the speed delta(T) approaches 0 as T -> infinity. The present contribution derives the aforementioned theore...
Let {chi(k)(t), t >= 0} be a stationary chi-process with k degrees of freedom being independent o...
A well-known property of stationary Gaussian processes is that the excursions over high levels ("pea...
AbstractLet {Xn} be a stationary Gaussian sequence with E{X0} = 0, {X20} = 1 and E{X0Xn} = rn n Let ...
With motivation from Husler (Extremes 7:179-190, 2004) and Piterbarg (Extremes 7:161-177, 2004) in t...
With motivation from Hüsler (Extremes 7:179-190, 2004) and Piterbarg (Extremes 7:161-177, 2004) in t...
Limit distributions of maxima of dependent Gaussian sequence are different according to the converge...
Limit distributions of maxima of dependent Gaussian sequence are different according to the converge...
We consider a Gaussian stationary process with Pickands' conditions and evaluate an exact asymptotic...
Let {X-i(t), t >= 0}, 1 <= i <= n be mutually independent centered Gaussian processes with ...
We derive the limiting distributions of exceedances point processes of randomly scaled weakly depend...
Pickands constants play a crucial role in the asymptotic theory of Gaussian processes. They are comm...
The principal results of this contribution are the weak and strong limits of maxima of contracted st...
AbstractIn this paper we study the asymptotic joint behavior of the maximum and the partial sum of a...
Abstract Consider a triangular array of mean zero Gaussian random variables. Under some weak conditi...
AbstractA well-known property of stationary Gaussian processes is that the excursions over high leve...
Let {chi(k)(t), t >= 0} be a stationary chi-process with k degrees of freedom being independent o...
A well-known property of stationary Gaussian processes is that the excursions over high levels ("pea...
AbstractLet {Xn} be a stationary Gaussian sequence with E{X0} = 0, {X20} = 1 and E{X0Xn} = rn n Let ...
With motivation from Husler (Extremes 7:179-190, 2004) and Piterbarg (Extremes 7:161-177, 2004) in t...
With motivation from Hüsler (Extremes 7:179-190, 2004) and Piterbarg (Extremes 7:161-177, 2004) in t...
Limit distributions of maxima of dependent Gaussian sequence are different according to the converge...
Limit distributions of maxima of dependent Gaussian sequence are different according to the converge...
We consider a Gaussian stationary process with Pickands' conditions and evaluate an exact asymptotic...
Let {X-i(t), t >= 0}, 1 <= i <= n be mutually independent centered Gaussian processes with ...
We derive the limiting distributions of exceedances point processes of randomly scaled weakly depend...
Pickands constants play a crucial role in the asymptotic theory of Gaussian processes. They are comm...
The principal results of this contribution are the weak and strong limits of maxima of contracted st...
AbstractIn this paper we study the asymptotic joint behavior of the maximum and the partial sum of a...
Abstract Consider a triangular array of mean zero Gaussian random variables. Under some weak conditi...
AbstractA well-known property of stationary Gaussian processes is that the excursions over high leve...
Let {chi(k)(t), t >= 0} be a stationary chi-process with k degrees of freedom being independent o...
A well-known property of stationary Gaussian processes is that the excursions over high levels ("pea...
AbstractLet {Xn} be a stationary Gaussian sequence with E{X0} = 0, {X20} = 1 and E{X0Xn} = rn n Let ...