We consider a model for multivariate data with heavy-tailed marginal distributions and a Gaussian dependence structure. The different marginals in the model are allowed to have non-identical tail behavior in contrast to most popular modeling paradigms for multivariate heavy-tail analysis. Despite being a practical choice, results on parameter estimation and inference under such models remain limited. In this article, consistent estimates for both marginal tail indices and the Gaussian correlation parameters for such models are provided and asymptotic normality of these estimators are established. The efficacy of the estimation methods are exhibited using extensive simulations and then they are applied to real data sets from insurance claims, internet traffic, and, online networks.

翻译：我们考虑一个具有重尾边缘分布和高斯相依结构的多元数据模型。与大多数流行的多元重尾分析建模范式不同，该模型中不同边缘被允许具有非相同的尾部行为。尽管这是一个实用的选择，但在此类模型下关于参数估计和推断的结果仍然有限。本文为该模型提供了边缘尾指数和高斯相关参数的一致估计，并确立了这些估计量的渐近正态性。通过大量模拟实验展示了估计方法的有效性，随后将其应用于保险索赔、互联网流量和在线网络等真实数据集。