Nagler, T., Krüger, D., Min, A.
Stationary vine copula models for multivariate time series

Meyer, D., Nagler, T., Hogan, R.J.
Copula-based synthetic data generation for machine learning emulators in weather and climate: application to a simple radiation model

Meyer, D., Nagler, T.
Synthia: multidimensional synthetic data generation in Python

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

Journal and conference papers

Czado, C. and Nagler, T. (2021)
Vine copula based modeling
Annual Review of Statistics and Its Application, to appear

Aas, K., Nagler, T., Jullum, M., Løland, A. (2021)
Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, to appear

Nagler, T. (2021)
R-friendly multi-threading in C++
Journal of Statistical Software, 97(c1)

Vio, R., Nagler, T., Andreani, P. (2020)
Modeling high-dimensional dependence among astronomical data
Astronomy & Astrophysics, 642, A156

Eggersmann, T.K., Baumeister, P., Kumbringk, J., Mayr, D., Schmoeckel, E., Thaler, C.J., Dannecker, C., Jeschke, U., Nagler, T., Mahner, S., Sharaf, K., and Gallwas, J.K.S. (2020)
Oropharyngeal HPV Detection Techniques in HPV-associated Head and Neck Cancer Patients.
Anticancer Research, 40(4):2117-2123

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. (2019)
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 - on the Tier-2 System CoolMUC; Garching/Munich 2018

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

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

Nagler, T. (2018)
Nonparametric estimation in simplified vine copula models
PhD thesis, 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