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Generative Archimedean Copulas

Publication ,  Journal Article
Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V
February 22, 2021

We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas. Specifically, we consider certain classes of copulas known as Archimedean and hierarchical Archimedean copulas, popular for their parsimonious representation and ability to model different tail dependencies. We consider their representation as mixture models with Laplace transforms of latent random variables from generative neural networks. This alternative representation allows for computational efficiencies and easy sampling, especially in high dimensions. We describe multiple methods for optimizing the network parameters. Finally, we present empirical results that demonstrate the efficacy of our proposed method in learning multidimensional CDFs and its computational efficiency compared to existing methods.

Duke Scholars

Publication Date

February 22, 2021
 

Citation

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Ng, Y., Hasan, A., Elkhalil, K., & Tarokh, V. (2021). Generative Archimedean Copulas.
Ng, Yuting, Ali Hasan, Khalil Elkhalil, and Vahid Tarokh. “Generative Archimedean Copulas,” February 22, 2021.
Ng Y, Hasan A, Elkhalil K, Tarokh V. Generative Archimedean Copulas. 2021 Feb 22;
Ng, Yuting, et al. Generative Archimedean Copulas. Feb. 2021.
Ng Y, Hasan A, Elkhalil K, Tarokh V. Generative Archimedean Copulas. 2021 Feb 22;

Publication Date

February 22, 2021