论文标题
极端分位数回归以比例的尾框架
Extreme quantile regression in a proportional tail framework
论文作者
论文摘要
我们重新审视了Einmahl等人最初引入的异质驱动器极端模型。 （JRSSB，2016年）描述了一个非固定序列的演变，该序列随着时间的流逝而演变并将其调整为一般的极端分位回归框架。我们提供了极值指数和综合的Skedasis函数的估计，并证明了它们的渐近态性。我们的结果与用于异质的极端开发的结果非常相似，但采用不同的证明方法强调耦合论点。我们还提出了Swedasis函数的点估计量和条件极端分位数的Weissman估计量，并证明了两个估计量的渐近正态性。
We revisit the model of heteroscedastic extremes initially introduced by Einmahl et al. (JRSSB, 2016) to describe the evolution of a non stationary sequence whose extremes evolve over time and adapt it into a general extreme quantile regression framework. We provide estimates for the extreme value index and the integrated skedasis function and prove their asymptotic normality. Our results are quite similar to those developed for heteroscedastic extremes but with a different proof approach emphasizing coupling arguments. We also propose a pointwise estimator of the skedasis function and a Weissman estimator of the conditional extreme quantile and prove the asymptotic normality of both estimators.
