High Performance Algorithms for Vine Copula Modeling in Python
High Performance Algorithms for Vine Copula Modeling in R
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect …
An R Package to Solve Estimating Equations with Copulas
Statistical Inference of Vine Copulas
High Performance Algorithms for Vine Copula Modeling
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure. First, an autoencoder (AE) compresses the data into a lower dimensional representation. …
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is …
We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high dimensional models remain parsimonious with pair copula constructions, and nest many …
We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables. We consider dependence or concordance measures that are solely functions of the copula, because …