Spectral density regression for bivariate extremes

Castro-Camilo, D. and de Carvalho, M. (2017) Spectral density regression for bivariate extremes. Stochastic Environmental Research and Risk Assessment, 31(7), pp. 1603-1613. (doi:10.1007/s00477-016-1257-z)

Full text not currently available from Enlighten.

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.

Item Type:Articles
Keywords:Bivariate extremes values, non-stationary extremal dependence structures, spectral density, statistics of extremes.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Castro Camilo, Dr Daniela
Authors: Castro-Camilo, D., and de Carvalho, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Stochastic Environmental Research and Risk Assessment
Journal Abbr.:SERRA
Publisher:Springer
ISSN:1436-3240
ISSN (Online):1436-3259
Published Online:11 May 2016

University Staff: Request a correction | Enlighten Editors: Update this record