
Let X(t), \(t\geq 0\), be a separable, measurable stationary (vector) process. A family of measurable sets \(A_ u\), \(u>0\), is called rare, if \(P(X(0)\in A_ u)\to 0\) as \(u\to \infty\), e.g., in the theory of extreme values of real valued processes \(A_ u=(u,\infty)\). The author presents generalizations of his earlier results on the asymptotic behaviour of the sojourn time of X(s), \(0\leq s\leq t\), in \(A_ u\), \(L_ t(u)=\int^{t}_{0}Ind_{\{X(s)\in A_ u\}}ds.\) In fact, a local sojourn theorem presented by the author in ibid. 10, 1- 46 (1982; Zbl 0498.60035) is generalized and it is shown that under specified conditions there exists a function v and a non-increasing function -\(\Gamma\) ' such that \[ P(v(u)L_ t(u)>x)/E(v(u)L_ t(u))\to -\Gamma '(x), \] x\(>0\), for \(u\to \infty\) and fixed \(t>0.\) The second main result is a global sojourn theorem stating that \(v(u)L_ t(u)\) is asymptotically compound Poisson distributed under a mixing condition on the family \(A_ u\) similar to the mixing condition of \textit{M. R. Leadbetter}, \textit{G. Lindgren} and \textit{H. Rootzén}, Extremes and related properties of random sequences and processes. (1983; Zbl 0518.60021). The results are applied to Markov processes and multivariate Gaussian processes.
limit distribution, asymptotic behaviour, Gaussian processes, extreme values, Sojourn, local sojourn theorem, Stationary stochastic processes, 60G15, Markov process, sojourn time, Gaussian process, stationary process, 60G10, Diffusion processes, mixing condition, 60J60
limit distribution, asymptotic behaviour, Gaussian processes, extreme values, Sojourn, local sojourn theorem, Stationary stochastic processes, 60G15, Markov process, sojourn time, Gaussian process, stationary process, 60G10, Diffusion processes, mixing condition, 60J60
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