Modeling the Extremes of Bivariate Mixture Distributions With Application to Oceanographic Data
针对双变量极值中由多个潜在过程驱动的混合依赖结构，指出现有方法的局限，提出两种新方法（条件极值模型扩展和极值分位数回归），模拟和海洋数据验证其优于传统方法。
There currently exist a variety of statistical methods for modeling bivariate extremes. However, when the dependence between variables is driven by more than one latent process, these methods are likely to fail to give reliable inferences. We consider situations in which the observed dependence at extreme levels is a mixture of a possibly unknown number of much simpler bivariate distributions. For such structures, we demonstrate the limitations of existing methods and propose two new methods: an extension of the Heffernan–Tawn conditional extreme value model to allow for mixtures and an extremal quantile-regression approach. The two methods are examined in a simulation study and then applied to oceanographic data. Finally, we discuss extensions including a subasymptotic version of the proposed model, which has the potential to give more efficient results by incorporating data that are less extreme. Both new methods outperform existing approaches when mixtures are present.