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2020 Bias correction in conditional multivariate extremes
Mikael Escobar-Bach, Yuri Goegebeur, Armelle Guillou
Electron. J. Statist. 14(1): 1773-1795 (2020). DOI: 10.1214/20-EJS1706


We consider bias-corrected estimation of the stable tail dependence function in the regression context. To this aim, we first estimate the bias of a smoothed estimator of the stable tail dependence function, and then we subtract it from the estimator. The weak convergence, as a stochastic process, of the resulting asymptotically unbiased estimator of the conditional stable tail dependence function, correctly normalized, is established under mild assumptions, the covariate argument being fixed. The finite sample behaviour of our asymptotically unbiased estimator is then illustrated on a simulation study and compared to two alternatives, which are not bias corrected. Finally, our methodology is applied to a dataset of air pollution measurements.


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Mikael Escobar-Bach. Yuri Goegebeur. Armelle Guillou. "Bias correction in conditional multivariate extremes." Electron. J. Statist. 14 (1) 1773 - 1795, 2020.


Received: 1 March 2019; Published: 2020
First available in Project Euclid: 22 April 2020

zbMATH: 07200243
MathSciNet: MR4090350
Digital Object Identifier: 10.1214/20-EJS1706

Primary: 62G05 , 62G20 , 62G32
Secondary: 60F05 , 60G70

Keywords: bias correction , conditional stable tail dependence function , Stochastic convergence


Vol.14 • No. 1 • 2020
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