一种用于空间数据的斜多变量分布类
A class of skew-multivariate distributions for spatial data
AI总结：
本文提出了一种基于多元帕累托混合分布的copula模型，用于空间数据，能够捕捉尾部依赖性和渐近独立性，并通过模拟和实际数据验证其有效性。
摘要：
本文介绍了一种基于多元帕累托混合分布的copula模型，用于空间数据。我们探讨了这些模型的尾部性质，证明了它们能够捕捉尾部依赖性和渐近独立性，以及现实中经常观察到的尾部不对称性。所提出的模型还提供了灵活的手段来考虑排列不对称性，并能有效表示分布的主体和极值尾部。我们考虑了这些模型的特殊案例，具有计算上可处理的似然函数，并进行了广泛的模拟研究，以评估最大似然估计量的有限样本性能。最后，我们应用我们的模型分析一个温度数据集，展示了其实际用途。
英文摘要：
This paper introduces a class of copula models for spatial data, based on multivariate Pareto-mixture distributions. We explore the tail properties of these models, demonstrating their ability to capture both tail dependence and asymptotic independence, as well as the tail asymmetry frequently observed in real-world data. The proposed models also offer flexibility in accounting for permutation asymmetry and can effectively represent both the bulk and extreme tails of the distribution. We consider special cases of these models with computationally tractable likelihoods and present an extensive simulation study to assess the finite-sample performance of the maximum likelihood estimators. Finally, we apply our models to analyze a temperature dataset, showcasing their practical utility.
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