Extremal clustering under moderate long range dependence and moderately heavy tails

dc.contributor.author	Chen, Zaoli
dc.contributor.author	Samorodnitsky, Gennady
dc.date.accessioned	2020-03-16T14:54:17Z
dc.date.available	2020-03-16T14:54:17Z
dc.date.issued	2020
dc.description.abstract	We study clustering of the extremes in a stationary sequence with subexponential tails in the maximum domain of attraction of the Gumbel We obtain functional limit theorems in the space of random sup- measures and in the space D(0, ∞). The limits have the Gumbel distribu- tion if the memory is only moderately long. However, as our results demon- strate rather strikingly, the “heuristic of a single big jump” could fail even in a moderately long range dependence setting. As the tails become lighter, the extremal behavior of a stationary process may depend on multiple large values of the driving noise.	en_US
dc.description.sponsorship	This research was partially supported by the ARO grant W911NF-18 -10318 at Cornell University	en_US
dc.identifier.uri	https://hdl.handle.net/1813/69698
dc.language.iso	en_US	en_US
dc.subject	extreme value theory	en_US
dc.subject	long range dependence	en_US
dc.subject	random sup-measure	en_US
dc.subject	stable regenerative set	en_US
dc.subject	subexponential tails	en_US
dc.subject	extremal clustering	en_US
dc.subject	Gumbel domain of attraction	en_US
dc.title	Extremal clustering under moderate long range dependence and moderately heavy tails	en_US
dc.type	article	en_US

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