Estimation and uncertainty quantification for extreme quantile regions
Estimation and uncertainty quantification for extreme quantile regions
复制标题
极端分位数区域的估计和不确定性量化
作者：
B. Beranger;S. Padoan;S. Sisson
极端分位数区域的估计是极端分析中的一项重要任务，极端分位数区域是未来极端事件可能以给定的低概率发生的空间，甚至超出了观测数据的范围。现有的方法来估计这些区域是可用的，但不提供任何估计不确定性的措施。我们开发了单变量和双变量方案估计极端分位数区域下的贝叶斯范式，优于现有的方法，并提供自然的措施分位数区域估计的不确定性。我们检查的方法的性能在控制模拟研究。我们说明了所提出的方法的适用性，通过分析高双变量分位数对污染物，有条件地在不同的温度梯度，记录在米兰，意大利。
Estimation of extreme quantile regions, spaces in which future extreme events can occur with a given low probability, even beyond the range of the observed data, is an important task in the analysis of extremes. Existing methods to estimate such regions are available, but do not provide any measures of estimation uncertainty. We develop univariate and bivariate schemes for estimating extreme quantile regions under the Bayesian paradigm that outperforms existing approaches and provides natural measures of quantile region estimate uncertainty. We examine the method’s performance in controlled simulation studies. We illustrate the applicability of the proposed method by analysing high bivariate quantiles for pairs of pollutants, conditionally on different temperature gradations, recorded in Milan, Italy.