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Thomas Nagler
Thomas Nagler
LMU Munich, Munich Center for Machine Learning
Verified email at lmu.de - Homepage
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Year
VineCopula: statistical inference of vine copulas
T Nagler, U Schepsmeier, J Stoeber, EC Brechmann, B Graeler, T Erhardt, ...
R package version 2.1.3, 2019
526*2019
Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
T Nagler, C Czado
Journal of Multivariate Analysis 151, 69-89, 2016
1922016
Vine copula based modeling
C Czado, T Nagler
Annual Review of Statistics and Its Application 9 (1), 453-477, 2022
1362022
kdecopula: An R package for the kernel estimation of bivariate copula densities
T Nagler
Journal of Statistical Software 84 (7), 1--22, 2018
89*2018
Model selection in sparse high-dimensional vine copula models with an application to portfolio risk
T Nagler, C Bumann, C Czado
Journal of Multivariate Analysis 172, 180-192, 2019
832019
rvinecopulib: High performance algorithms for vine copula modeling
T Nagler, T Vatter
R package version, 2018
70*2018
Nonparametric estimation of simplified vine copula models: comparison of methods
T Nagler, C Schellhase, C Czado
Dependence Modeling 5 (1), 99-120, 2017
672017
Generalized additive models for pair-copula constructions
T Vatter, T Nagler
Journal of Computational and Graphical Statistics 27 (4), 715-727, 2018
562018
Stationary vine copula models for multivariate time series
T Nagler, D Krüger, A Min
Journal of Econometrics 227 (2), 305-324, 2022
442022
Kernel methods for vine copula estimation
T Nagler
Master's thesis, Technical University of Munich, 2014
402014
Copula-based synthetic data augmentation for machine-learning emulators
D Meyer, T Nagler, RJ Hogan
Geoscientific Model Development 14 (8), 5205-5215, 2021
36*2021
D-vine quantile regression with discrete variables
N Schallhorn, D Kraus, T Nagler, C Czado
arXiv preprint arXiv:1705.08310, 2017
362017
A generic approach to nonparametric function estimation with mixed data
T Nagler
Statistics & Probability Letters 137, 326-330, 2018
352018
A statistical simulation method for joint time series of non-stationary hourly wave parameters
WS Jäger, T Nagler, C Czado, RT McCall
Coastal Engineering 146, 14-31, 2019
292019
vinereg: D-vine quantile regression
T Nagler, D Kraus
R package version 70, 2019
282019
Kde1d: Univariate kernel density estimation
T Nagler, T Vatter
R package version 1 (2), 6, 2019
262019
Explaining predictive models using Shapley values and non-parametric vine copulas
K Aas, T Nagler, M Jullum, A Løland
Dependence modeling 9 (1), 62-81, 2021
252021
Asymptotic analysis of the jittering kernel density estimator
T Nagler
Mathematical Methods of Statistics 27, 32-46, 2018
232018
Statistical foundations of prior-data fitted networks
T Nagler
The 40th International Conference on Machine Learning (ICML 2023), 2023
212023
Synthia: multidimensional synthetic data generation in Python
D Meyer, T Nagler
The Journal of Open Source Software 460, 2021
192021
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