74 citations · 179 across the 25 of their papers we have counts for
5 papers · 1 filter
D-vine quantile regression with discrete variables
Niklas Schallhorn, Daniel Kraus, Thomas Nagler +1
Quantile regression, the prediction of conditional quantiles, finds applications in various fields. Often, some or all of the variables are discrete. The authors propose two new qu…
Growing simplified vine copula trees: improving Dißmann's algorithm
Daniel Kraus, Claudia Czado
Vine copulas are pair-copula constructions enabling multivariate dependence modeling in terms of bivariate building blocks. One of the main tasks of fitting a vine copula is the se…
Using model distances to investigate the simplifying assumption, model selection and truncation levels for vine copulas
Matthias Killiches, Daniel Kraus, Claudia Czado
Vine copulas are a useful statistical tool to describe the dependence structure between several random variables, especially when the number of variables is very large. When modeli…
D-vine copula based quantile regression
Daniel Kraus, Claudia Czado
Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. The authors introduce a new…
Model distances for vine copulas in high dimensions
Matthias Killiches, Daniel Kraus, Claudia Czado
Vine copulas are a flexible class of dependence models consisting of bivariate building blocks and have proven to be particularly useful in high dimensions. Classical model distanc…