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Spectral density regression for bivariate extremes
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DOI：10.1007/s00477-016-1257-z.png)
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摘要
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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.
Keyword:
Bivariate extremes values
Nonstationary extremal dependence structures
Spectral density
Statistics of extremes
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