Penalized estimation of hierarchical Archimedean copula
提出一种同时估计分层阿基米德连接函数参数和结构的新方法，通过非凹惩罚实现，并研究了估计量的渐近性质和小样本表现。
This manuscript discusses a novel estimation approach for parametric hierarchical Archimedean copula. The parameters and structure of this copula are simultaneously estimated while imposing a non-concave penalty on differences between parameters which coincides with an implicit penalty on the copula’s structure. The asymptotic properties of the resulting penalized estimator are studied and small sample properties are illustrated using simulations.