Inference and Sampling for Archimax Copulas

@article{Ng2022InferenceAS,
  title={Inference and Sampling for Archimax Copulas},
  author={Yuting Ng and Ali Hasan and Vahid Tarokh},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.14025}
}
Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas are a family of distributions endowed with a precise representation that allows simultaneous modeling of the bulk and the tails of a distribution. Rather than separating the two as is typically done in practice, incorporating additional… 

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