Sojourns of Stationary Gaussian Processes over a Random Interval

TL;DR

This paper analyzes the asymptotic behavior of the tail distribution of the total time a stationary Gaussian process exceeds a high level over a random interval, revealing how the tail heaviness of the interval influences the results.

Contribution

It provides a comprehensive asymptotic analysis of sojourn times for Gaussian processes over random intervals with various tail behaviors, including new scenarios.

Findings

Different asymptotic forms depending on the tail heaviness of T

Four distinct cases based on the tail distribution of T

Application to fractional Ornstein-Uhlenbeck processes

Abstract

We investigate asymptotics of the tail distribution of sojourn time $$ \int_0^T \mathbb{I}(X(t)> u)dt, $$ as $u\to\infty$, where $X$ is a centered stationary Gaussian process and $T$ is an independent of $X$ nonnegative random variable. The heaviness of the tail distribution of $T$ impacts the form of the asymptotics, leading to four scenarios: the case of integrable $T$, the case of regularly varying $T$ with index $\lambda=1$ and index $\lambda\in(0,1)$ and the case of slowly varying tail distribution of $T$. The derived findings are illustrated by the analysis of the class of fractional Ornstein-Uhlenbeck processes.