Extreme Value Theory for Continuous Parameter Stationary Processes.
Abstract
In this paper the central distributional results of classical extreme value theory are obtained, under appropriate dependence restrictions, for maxima of continuous parameter stochastic processes. In particular we prove the basic result (here called Gnedenko's Theorem) concerning the existence of just three types of non-degenerate limiting distributions in such cases, and give necessary and sufficient conditions for each to apply. The development relies, in part, on the corresponding known theory for stationary sequences. The general theory given does not finiteness of the number of upcrossings of any level x. However when the number per unit time is a.s. finite and has a finite mean mu(x), it is found that the classical criteria for domains of attraction apply when mu(x) is used in lieu of the tail of the marginal distribution function. The theory is specialized to this case and applied to give the general known results for stationary normal processes (for which mu(x) may or may not be finite). A general Poisson convergence theorem is given for high level upcrossings, together with its implications for the asymptotic distributions of r-th local maxima. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1980
- Accession Number
- ADA083802
Entities
People
- M. Ross Leadbetter
Organizations
- University of North Carolina at Chapel Hill