High Performance Algorithms for Vine Copula Modeling in Python


High Performance Algorithms for Vine Copula Modeling in R

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

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

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

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. …

Generative Models for Simulating Mobility Trajectories

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 …

Dependent Defaults and Losses with Factor Copula Models

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 …

Generalized additive models for conditional dependence structures

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 …