Latent Tree Copulas
نویسنده
چکیده
We propose a new approach for estimation of joint densities for continuous observations using latent tree models for copulas, joint distributions with uniform U (0, 1) marginals. Latent tree copulas combine the advantages of the parametrization of the joint density using only bivariate distributions with the ability to approximate complex dependencies with the help of latent variables. The proposed model can also be used to organize the variables in a tree hierarchy. We describe algorithms for estimating binary latent tree copulas from data for both Gaussian and non-Gaussian copulas.
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