论文标题
在极端温度数据中应用超级依赖性的短距离依赖性
Modeling short-ranged dependence in block extrema with application to polar temperature data
论文作者
论文摘要
块最大值方法是单变量极值分析的重要方法。在假设块最大值是独立的结果中，可以直接分析，但当一系列块最大值表现出依赖性时，结果推论可能无效。我们提出了一个基于一阶马尔可夫假设的模型，该模型通过使用双变量逻辑依赖性结构在连续的块最大值之间结合了依赖性，同时维持了概括性的极值（GEV）边缘分布。当块最大值表现出短距离依赖性时，以这种方式建模依赖性使我们能够更好地估计极端分位数。我们通过一项模拟研究证明，当连续的块最大值依赖时，我们的一阶Markov GEV模型的表现良好，而当最大值是独立时，同时仍然相当健壮。我们将我们的方法应用于表现出短距离依赖性结构的两个极性年度最低空气温度数据集，并发现所提出的模型得出了对高分子的修改估计。
The block maxima approach is an important method in univariate extreme value analysis. While assuming that block maxima are independent results in straightforward analysis, the resulting inferences maybe invalid when a series of block maxima exhibits dependence. We propose a model, based on a first-order Markov assumption, that incorporates dependence between successive block maxima through the use of a bivariate logistic dependence structure while maintaining generalized extreme value (GEV) marginal distributions. Modeling dependence in this manner allows us to better estimate extreme quantiles when block maxima exhibit short-ranged dependence. We demonstrate via a simulation study that our first-order Markov GEV model performs well when successive block maxima are dependent, while still being reasonably robust when maxima are independent. We apply our method to two polar annual minimum air temperature data sets that exhibit short-ranged dependence structures, and find that the proposed model yields modified estimates of high quantiles.
