Publications
Preprints
Herbinger, Wright, Nagler, Bischl, and Casalicchio. Decomposing Global Feature Effects Based on Feature Interactions. [pdf]
Nagler, Schneider, Bischl, and Feurer. Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. [pdf]
2024
Palm and Nagler. An Online Bootstrap for Time Series. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS) [pdf] [doi]
Rügamer, Kolb, Weber, Kook, and Nagler. Generalizing Orthogonalization for Models with Non-linearities. The 41st International Conference on Machine Learning (ICML). [pdf]
Sale, Hofman, Löhr, Wimmer, Nagler, and Hüllermeier. Label-wise Aleatoric and Epistemic Uncertainty Quantification. The 40th Conference on Uncertainty in Artificial Intelligence (UAI) [pdf]
Rundel, Kobialka, von Crailsheim, Feurer, Nagler, and Rügamer. Interpretable Machine Learning for TabPFN. The 2nd World Conference on eXplainable Artificial Intelligence (XAI) [pdf] [doi]
2023
Nagler and Vatter. Solving estimating equations with copulas. Journal of the American Statistical Association 119(546), 1168–1180. [pdf] [doi]
Nagler. Statistical foundations of prior-data fitted networks. The 40th International Conference on Machine Learning (ICML), PMLR 202:25660-25676, 2023. [pdf] [doi]
Rodemann, Goschenhofer, Dorigatti, Nagler, and Augustin. Approximately Bayes-Optimal Pseudo Label Selection. The 39th Conference on Uncertainty in Artificial Intelligence (UAI) [pdf] [doi]
Zwep, Guo, Nagler, Knibbe, Meulman, and van Hasselt. Virtual Patient Simulation Using Copula Modeling. Clinical Pharmacology & Therapeutics. [pdf] [doi]
Sale, Hofman, Wimmer, Hüllermeier, and Nagler. Second-Order Uncertainty Quantification: Variance-Based Measures. Research report [pdf]
2022
Nagler, Krüger, and Min. Stationary vine copula models for multivariate time series. Journal of Econometrics, 227(2):305-324 [doi] [pdf] [suppl.]
Czado and Nagler. Vine copula based modeling. Annual Review of Statistics and Its Application 9, pp. 453-477 [doi] [pdf]
Czado, Bax, Sahin, Nagler, Min, and Paterlini. Vine copula based dependence modeling in sustainable finance. The Journal of Finance and Data Science, 8:309.-330 [doi] [pdf]
Lotto, Nagler, and Radic. Modeling Stochastic Data Using Copulas For Application in Validation of Autonomous Driving. Electronics, 11(24):4154 [doi] [pdf]
Nguyen-Huy, Kath, Nagler, Khaung, Aung, Mushtaq, Marcussen, and 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
Meyer, Nagler, and Hogan. Copula-based synthetic data augmentation for machine learning emulators. Geoscientific Model Development 14(8):5205–5215 [doi] [pdf]
Aas, Nagler, Jullum, and Løland. Explaining predictive models using Shapley values and non-parametric vine copulas. Dependence Modeling, 9:62–81 [doi] [pdf]
Nagler. R-friendly multi-threading in C++. Journal of Statistical Software, 97(c1) [doi] [pdf]
Meyer and Nagler. Synthia: multidimensional synthetic data generation in Python. Journal of Open Source Software, 6(65), 2863 [doi] [pdf]
2020
Vio, Nagler, and Andreani. Modeling high-dimensional dependence among astronomical data. Astronomy & Astrophysics, 642, A156 [doi] [pdf]
Eggersmann, Baumeister, Kumbringk, Mayr, Schmoeckel, Thaler, Dannecker, Jeschke, Nagler, Mahner, Sharaf, and Gallwas. Oropharyngeal HPV Detection Techniques in HPV-associated Head and Neck Cancer Patients. Anticancer Research, 40(4):2117-2123 [doi]
2019
Nagler, Bumann, and Czado. Model selection for sparse high-dimensional vine copulas with application to portfolio risk. Journal of Multivariate Analysis, 172: 180-192 [doi] [pdf]
Jäger, Nagler, Czado, and McCall. A statistical simulation method for joint time series of non-stationary hourly wave parameters. Coastal Engineering, 146: 14-31 [doi] [pdf]
2018
Urbano and 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]
Vatter and Nagler. Generalized additive models for pair-copula constructions. Journal of Computational and Graphical Statistics, 27(4): 715-727 [doi] [pdf]
Nagler. A generic approach to nonparametric function estimation with mixed data. Statistics & Probability Letters, 137:326–330 [doi] [pdf]
Nagler. Asymptotic analysis of the jittering kernel density estimator. Mathematical Methods of Statistics, 27(1): 32-46 [doi] [pdf]
Nagler. kdecopula: An R package for the kernel estimation of copula densities. Journal of Statistical Software, 48(7) [doi] [pdf]
Höhndorf, Nagler, Koppitz, Czado, and Holzapfel. Statistical dependence analyses of operational flight data used for landing reconstruction enhancement. The 22nd Air Transport Research Society World Conference (ATRS 2018) [pdf]
Czado, Müller, and 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]
Möller, Spazzini, Kraus, Nagler, and Czado. Vine copula based post-processing of ensemble forecasts for temperature. Research report [pdf]
Schallhorn, Kraus, Nagler, and Czado. D-vine quantile regression with discrete variables. Research report [pdf]
Nagler. Nonparametric estimation in simplified vine copula models. PhD thesis, Technical University of Munich [pdf]
2017
Nagler, Schellhase, and Czado. Nonparametric estimation of simplified vine copula models: comparison of methods. Dependence Modeling, 5:99-120 [doi] [pdf]
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]
Kreuzer, Nagler, and Czado. Heavy tailed spatial autocorrelation models. Research report [pdf]
2016
Nagler and Czado. Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas. Journal of Multivariate Analysis, 151:69-89 [doi] [pdf]
2014
Nagler. Kernel methods for vine copula estimation. Master’s thesis, Technical University of Munich [pdf]