151 citations · 321 across the 18 of their papers we have counts for
12 papers · 1 filter
Explaining predictive models using Shapley values and non-parametric vine copulas
Kjersti Aas, Thomas Nagler, Martin Jullum +1
The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are de…
Stationary vine copula models for multivariate time series
Thomas Nagler, Daniel Krüger, Aleksey Min
Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture…
Modeling high-dimensional dependence among astronomical data
Roberto Vio, Thomas W. Nagler, Paola Andreani
Fixing the relationship of a set of experimental quantities is a fundamental issue in many scientific disciplines. In the 2D case, the classical approach is to compute the linear c…
Solving estimating equations with copulas
Thomas Nagler, Thibault Vatter
Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. Mor…
Model selection in sparse high-dimensional vine copula models with application to portfolio risk
Thomas Nagler, Christian Bumann, Claudia Czado
Vine copulas allow to build flexible dependence models for an arbitrary number of variables using only bivariate building blocks. The number of parameters in a vine copula model in…
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…
Asymptotic analysis of the jittering kernel density estimator
Thomas Nagler
Jittering estimators are nonparametric function estimators for mixed data. They extend arbitrary estimators from the continuous setting by adding random noise to discrete variables…
A generic approach to nonparametric function estimation with mixed data
Thomas Nagler
In practice, data often contain discrete variables. But most of the popular nonparametric estimation methods have been developed in a purely continuous framework. A common trick am…
Nonparametric estimation of simplified vine copula models: comparison of methods
Thomas Nagler, Christian Schellhase, Claudia Czado
In the last decade, simplified vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densities an…
Generalized Additive Models for Pair-Copula Constructions
Thibault Vatter, Thomas Nagler
Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks. In this paper, we use generalized additive models to extend them by allowing…
Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
Thomas Nagler, Claudia Czado
Practical applications of nonparametric density estimators in more than three dimensions suffer a great deal from the well-known curse of dimensionality: convergence slows down as…