Home > Research > Publications & Outputs > Conditioned limit laws for inverted max-stable ...

Electronic data

  • ims_conditioned_revised2

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 150, 2016 DOI: 10.1016/j.jmva.2016.06.001

    Accepted author manuscript, 757 KB, PDF-document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Conditioned limit laws for inverted max-stable processes

Research output: Contribution to journalJournal article

Published
Close
<mark>Journal publication date</mark>09/2016
<mark>Journal</mark>Journal of Multivariate Analysis
Volume150
Number of pages15
Pages (from-to)214-228
<mark>State</mark>Published
Early online date22/06/16
<mark>Original language</mark>English

Abstract

Max-stable processes are widely used to model spatial extremes. These processes exhibit asymptotic dependence meaning that the large values of the process can occur simultaneously over space. Recently, inverted max-stable processes have been proposed as an important new class for spatial extremes which are in the domain of attraction of a spatially independent max-stable process but instead they cover the broad class of asymptotic independence. To study the extreme values of such processes we use the conditioned approach to multivariate extremes that characterises the limiting distribution of appropriately normalised random vectors given that at least one of their components is large. The current statistical methods for the conditioned approach are based on a canonical parametric family of location and scale norming functions. We study broad classes of inverted max-stable processes containing processes linked to the widely studied max-stable models of Brown-Resnick and extremal-tt, and identify conditions for the normalisations to either belong to the canonical family or not. Despite such differences at an asymptotic level, we show that at practical levels, the canonical model can approximate well the true conditional distributions.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 150, 2016 DOI: 10.1016/j.jmva.2016.06.001