Spectral density regression for bivariate extremes

Daniela Castro Camilo, Miguel de Carvalho

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
Original languageEnglish (US)
Pages (from-to)1603-1613
Number of pages11
JournalStochastic Environmental Research and Risk Assessment
Volume31
Issue number7
DOIs
StatePublished - May 11 2016

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