文章基本信息

标题：Bias correction in conditional multivariate extremes
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作者：Mikael Escobar-Bach ; Yuri Goegebeur ; Armelle Guillou 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2020
卷号：14
期号：1
页码：1773-1795
DOI：10.1214/20-EJS1706
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.
关键词：Bias correction; conditional stable tail dependence function; stochastic convergence