Huang Mei Ling, Nguyen Christine
Department of Mathematics & Statistics, Brock University, St. Catharines, Ontario, Canada.
J Stat Distrib Appl. 2017;4(1):4. doi: 10.1186/s40488-017-0058-3. Epub 2017 May 3.
For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an -loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.
对于极端事件，重尾分布的高条件分位数估计是一个重要问题。分位数回归是该领域一种有许多应用的有用方法。分位数回归使用 - 损失函数，并通过线性规划得到最优解。在本文中，我们提出了一种加权分位数回归方法。进行蒙特卡罗模拟以将所提出的方法与用于估计高条件分位数的现有方法进行比较。我们还通过使用所提出的加权方法研究了两个实际例子。蒙特卡罗模拟和两个实际例子表明所提出的方法是对现有方法的改进。
