17 citations · 31 across the 9 of their papers we have counts for
10 papers
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso
Dominik Müller, Claudia Czado
To model high dimensional data, Gaussian methods are widely used since they remain tractable and yield parsimonious models by imposing strong assumptions on the data. Vine copulas…
Heavy tailed spatial autocorrelation models
A. Kreuzer, T. Erhardt, T. Nagler +1
Appropriate models for spatially autocorrelated data account for the fact that observations are not independent. A popular model in this context is the simultaneous autoregressive…
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…
A D-vine copula based model for repeated measurements extending linear mixed models with homogeneous correlation structure
Matthias Killiches, Claudia Czado
We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine co…
Selection of Sparse Vine Copulas in High Dimensions with the Lasso
Dominik Müller, Claudia Czado
We propose a novel structure selection method for high dimensional (d > 100) sparse vine copulas. Current sequential greedy approaches for structure selection require calculating s…
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…