论文标题
一种贝叶斯半参数杂交模型，用于空间极端的依赖性结构未知的极端
A Bayesian semi-parametric hybrid model for spatial extremes with unknown dependence structure
论文作者
论文摘要
最大稳定过程是空间极端的渐近合理模型。特别是，我们专注于分层极值过程（HEVP），这是一个有利于贝叶斯计算的特定最大稳定过程。 HEVP和所有最大稳定过程模型都是参数性的，并施加了强有力的假设，包括所有边际分布都属于广义的极值家族，并且附近的站点渐近依赖。我们通过放松这些假设，通过在空间随机效应分布的事先提前提供一类更广泛的边际分布来概括HEVP。此外，我们提出了一个混合最大混合模型，该模型结合了参数和半参数模型的强度。我们表明，这种多功能最大混合模型可容纳渐近独立性和依赖性，并且可以使用标准的马尔可夫链蒙特卡洛算法拟合。我们的模型的实用性在蒙特卡洛模拟研究和荷兰风阵阵风数据中进行了评估。
The max-stable process is an asymptotically justified model for spatial extremes. In particular, we focus on the hierarchical extreme-value process (HEVP), which is a particular max-stable process that is conducive to Bayesian computing. The HEVP and all max-stable process models are parametric and impose strong assumptions including that all marginal distributions belong to the generalized extreme value family and that nearby sites are asymptotically dependent. We generalize the HEVP by relaxing these assumptions to provide a wider class of marginal distributions via a Dirichlet process prior for the spatial random effects distribution. In addition, we present a hybrid max-mixture model that combines the strengths of the parametric and semi-parametric models. We show that this versatile max-mixture model accommodates both asymptotic independence and dependence and can be fit using standard Markov chain Monte Carlo algorithms. The utility of our model is evaluated in Monte Carlo simulation studies and application to Netherlands wind gust data.
