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, 172: 180-192 [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