Estimation of extreme conditional quantiles under a general tail-first-order condition
نویسندگان
چکیده
منابع مشابه
Nonparametric estimation of the conditional tail index and extreme quantiles under random censoring
In this paper, we investigate the estimation of the tail index and extreme quantiles of a heavy-tailed distribution when some covariate information is available and the data are randomly right-censored. We construct several estimators by combining a moving-window technique (for tackling the covariate information) and the inverse probability-of-censoring weighting method, and we establish their ...
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The estimation of extreme conditional quantiles is an important issue in different scientific disciplines. Up to now, the extreme value literature focused mainly on estimation procedures based on i.i.d. samples. On the other hand, quantile regression based procedures work well for estimation within the data range i.e. the estimation of nonextreme quantiles but break down when main interest is i...
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− We address the estimation of quantiles from heavy-tailed distributions when functional covariate information is available and in the case where the order of the quantile converges to one as the sample size increases. Such ”extreme” quantiles can be located in the range of the data or near and even beyond the boundary of the sample, depending on the convergence rate of their order to one. Nonp...
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We address the estimation of “extreme” conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed kernel estimators. A Weissman-type estimator and kernel estimators of the conditional tailindex are derived, permitting to estimate extreme conditio...
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ژورنال
عنوان ژورنال: Annals of the Institute of Statistical Mathematics
سال: 2019
ISSN: 0020-3157,1572-9052
DOI: 10.1007/s10463-019-00713-7