Publications

2024

J. Herbinger, M. Wright, T. Nagler, B. Bischl, G. Casalicchio. Decomposing Global Feature Effects Based on Feature Interactions. Preprint [pdf]

T. Nagler, L. Schneider, B. Bischl, M. Feurer. Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. Preprint [pdf]

N. Palm, T. Nagler. An Online Bootstrap for Time Series. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS) [pdf] [doi]

D. Rügamer, C. Kolb, T. Weber, L. Kook, T. Nagler. Generalizing Orthogonalization for Models with Non-linearities. The 41st International Conference on Machine Learning (ICML). [pdf]

Y. Sale, P. Hofman, T. Löhr, L. Wimmer, T. Nagler, E. Hüllermeier. Label-wise Aleatoric and Epistemic Uncertainty Quantification. The 40th Conference on Uncertainty in Artificial Intelligence (UAI) [pdf]

D. Rundel, J. Kobialka, C. von Crailsheim, M. Feurer, T. Nagler, D. Rügamer. Interpretable Machine Learning for TabPFN. The 2nd World Conference on eXplainable Artificial Intelligence (XAI) [pdf] [dois]

2023

T. Nagler, T. Vatter. Solving estimating equations with copulas. Journal of the American Statistical Association 119(546), 1168–1180. [pdf] [doi]

Y. Sale, P. Hofman, L. Wimmer, E. Hüllermeier, T. Nagler. Second-Order Uncertainty Quantification: Variance-Based Measures. preprint [pdf]

L. Zwep. T. Guo, T. Nagler, C. A.J. Knibbe, J. J. Meulman, J. G. Coen van Hasselt. Virtual Patient Simulation Using Copula Modeling. Clinical Pharmacology & Therapeutics. [pdf] [doi]

J. Rodemann, J. Goschenhofer, E. Dorigatti, T. Nagler, T. Augustin. Approximately Bayes-Optimal Pseudo Label Selection. The 39th Conference on Uncertainty in Artificial Intelligence (UAI) [pdf]

T. Nagler. Statistical foundations of prior-data fitted networks. Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR 202:25660-25676, 2023. [pdf] [doi]

2022

C. Czado, K. Bax, Ö. Sahin, T. Nagler, A. Min, S. Paterlini. Vine copula based dependence modeling in sustainable finance. The Journal of Finance and Data Science, 8:309.-330 [doi] [pdf]

T. Nagler, D. Krüger, A. Min. Stationary vine copula models for multivariate time series. Journal of Econometrics, 227(2):305-324 [doi] [pdf] [suppl.]

C. Czado, T. Nagler. Vine copula based modeling. Annual Review of Statistics and Its Application 9, pp. 453-477 [doi] [pdf]

K. Lotto, T. Nagler, M. Radic. Modeling Stochastic Data Using Copulas For Application in Validation of Autonomous Driving. Electronics, 11(24):4154 [doi] [pdf]

T. Nguyen-Huy, J. Kath, T. Nagler, Y. Khaung, T.S.S. Aung, S. Mushtaq, T. Marcussen, R. Stone. A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar. Remote Sensing Applications: Society and Environment, 26 [doi] [pdf]

2021

D. Meyer, T. Nagler. Synthia: multidimensional synthetic data generation in Python. Journal of Open Source Software, 6(65), 2863 [doi] [pdf]

D. Meyer, T. Nagler, R.J. Hogan. Copula-based synthetic data augmentation for machine learning emulators. Geoscientific Model Development 14(8):5205–5215 [doi] [pdf]

K. Aas, T. Nagler, M. Jullum, A. Løland. Explaining predictive models using Shapley values and non-parametric vine copulas. Dependence Modeling, 9:62–81 [doi] [pdf]

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

2020

R. Vio, T. Nagler, P. Andreani. Modeling high-dimensional dependence among astronomical data. Astronomy & Astrophysics, 642, A156 [doi] [pdf]

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

2019

T. Nagler, C. Bumann, C. Czado. Model selection for sparse high-dimensional vine copulas with application to portfolio risk. Journal of Multivariate Analysis, 172: 180-192 [doi] [pdf]

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

2018

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

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

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

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

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

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

C. Czado, D. Müller, T. Nagler. 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 [pdf]

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

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

T. Nagler. Nonparametric estimation in simplified vine copula models. PhD thesis, Technical University of Munich [pdf]

2017

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

T. Nagler. 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] [pdf]

A. Kreuzer, T. Nagler, C. Czado. Heavy tailed spatial autocorrelation models. Research report [pdf]

2016

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

2014

T. Nagler. Kernel methods for vine copula estimation. Master’s thesis, Technical University of Munich [pdf]