On kernel estimation of the second order rate parameter in multivariate extreme value statistics
Copyright policy )On kernel estimation of the second order rate parameter in multivariate extreme value statistics
We introduce a flexible class of kernel type estimators of a second order parameter appearing in the multivariate extreme value framework. Such an estimator is crucial in order to construct asymptotically unbiased estimators of dependence measures, as e.g. the stable tail dependence function. We establish the asymptotic properties of this class of estimators under suitable assumptions. The behaviour of some examples of kernel estimators is illustrated by a simulation study in which they are also compared with a benchmark estimator of a second order parameter recently introduced in the literature.
- University of Southern Denmark Denmark
- University of Strasbourg France
- University of Strasbourg France
- UnivOAK France
- French National Centre for Scientific Research France
Second order parameter, Statistics of extreme values; tail inference, Density estimation, multivariate extreme value statistics, Asymptotic properties of nonparametric inference, Stable tail dependence function, stable tail dependence function, second order parameter, Multivariate extreme value statistics, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Second order parameter, Statistics of extreme values; tail inference, Density estimation, multivariate extreme value statistics, Asymptotic properties of nonparametric inference, Stable tail dependence function, stable tail dependence function, second order parameter, Multivariate extreme value statistics, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
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