Nagler, T., Vatter, T.
Solving estimating equations with copulas

Möller, A., Spazzini, L., Kraus, D., Nagler, T., Czado, C.
Vine copula based post-processing of ensemble forecasts for temperature

Schallhorn, N., Kraus, D., Nagler, T. and Czado, C.
D-vine quantile regression with discrete variables

Journal and conference papers

Nagler, T.
R-friendly multi-threading in C++
Journal of Statistical Software, to appear

Nagler, T., Bumann, C., Czado, C. (2019)
Model selection for sparse high-dimensional vine copulas with application to portfolio risk
Journal of Multivariate Analysis, in press [doi]

Jäger, W.S., Nagler, T., Czado, C., McCall, R.T. (2018)
A statistical simulation method for joint time series of non-stationary hourly wave parameters
Coastal Engineering, 146: 14-31 [doi]

Urbano, J., Nagler, T. (2018)
Stochastic simulation of test collections: evaluation scores
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, p. 695-704 [doi]

Höhndorf, L., Nagler, T., Koppitz, P., Czado, C., Holzapfel, F. (2018)
Statistical dependence analyses of operational flight data used for landing reconstruction enhancement
22nd ATRS World Conference

Vatter, T. and Nagler, T. (2018)
Generalized additive models for pair-copula constructions
Journal of Computational and Graphical Statistics, 27(4): 715-727 [doi]

Nagler, T. (2018)
A generic approach to nonparametric function estimation with mixed data
Statistics & Probability Letters, 137:326–330 [doi]

Nagler, T. (2018)
Asymptotic analysis of the jittering kernel density estimator
Mathematical Methods of Statistics, 27(1): 32-46 [doi]

Nagler, T. (2018)
kdecopula: An R package for the kernel estimation of copula densities
Journal of Statistical Software, 48(7) [doi]

Nagler, T., Schellhase, C. and Czado, C. (2017)
Nonparametric estimation of simplified vine copula models: comparison of methods
Dependence Modeling, 5:99-120 [doi]

Nagler, T. and Czado, C. (2016)
Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
Journal of Multivariate Analysis, 151:69-89 [doi]


Czado, C., Müller, D., Nagler, T. (2018)
Dependence modelling in ultra high dimensions with vine copulas
Book chapter in High Performance Computing in Science and Engineering; Garching/Munich 2018, to appear

Nagler, T. (2018)
Nonparametric estimation in simplified vine copula models
Dissertation, Technical University of Munich

Nagler, T. (2017)
Comment on “A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis” by Minh Tu Pham et al.
Interactive comment on Hydrol. Earth Syst. Sci. Discuss. [doi]

Nagler, T. (2014)
Kernel methods for vine copula estimation
Master’s thesis, Technical University of Munich



vinecopulib: A C++ library for vine copulas

RcppThread: R-friendly threading in C++

wdm: Efficient implementation of weighted dependence measures and related independence tests


rvinecopulib: R interface to the vinecopulib C++ library

VineCopula: Statistical inference of vine copulas

wdm: R interface to the wdm C++ library

kde1d: Univariate kernel density estimators for bounded and discrete data

vinereg: D-vine copula quantile regression

VC2copula: Extend the ‘copula’ Package with Families and Models from ‘VineCopula’

kdecopula: Kernel smoothing for bivariate copula densities

kdevine: Multivariate kernel density estimation with vine copulas

cctols: Tools for continuous convolution in nonparametric estimation

jdify: Joint density classifiers

gamCopula: Generalized additive models for bivariate conditional dependence structures and vine copulas



Nonparametric statistical learning, TU Munich (Summer 2019)

Teaching assistant

Analysis for computer scientists, TU Munich (Winter 2018)
Stochastics for vocational school teachers, TU Munich (Summer 2018)
Computational statistics, TU Munich (Summer 2018)
Rank-based nonparametric statistics, TU Munich (Summer 2016)

Seminars for Master’s students

Functional data analysis, TU Munich (Summer 2017)
Mathematical introduction to neural networks, TU Munich (Winter 2016)
Nonparametric statistical methods, TU Munich (Summer 2016)

Supervision of Master’s theses

Krüger, D. (2018). General vine copula models for stationary multivariate time series.
Weber, R. (2018). Value at Risk Estimation with Subset Simulation.
Jebabli, E. (2018). Statistical methods for the objective assessment of subjective ratings of advanced driver assistance systems.
Kreuzer, A. (2016). Analysing the spatial dependency among fire danger indices.
Brauer, A. (2016). Kernel estimation of conditional copula densities.
Bumann, C. (2016). Sparse structure selection for high-dimensional vine copula models.
Annoh, I. (2016). Comparing two-part models for estimation of actuarial total loss.
Zhang, S. (2015). Copula-based total loss estimation with group effects on the dependence structure.


  • Mathematical Institute, Leiden University, Niels Bohrweg 1 (Room 234), 2333 CA Leiden, Netherlands