I am Assistant Professor for Statistical Science at the Mathematical Institute, Leiden University. In 2018, I obtained a PhD in Mathematical Statistics from TU Munich under the supervision of Claudia Czado.
My methodological research mainly focuses on dependence models and nonparametric estimation. I also develop open source research software and collaborate with applied scientists to find statistical answers to their challenges.
PhD Mathematical Statistics, 2018
Technical University of Munich
MSc Mathematical Finance and Actuarial Sciences, 2014
Technical University of Munich
BSc Mathematics, 2012
Technical University of Munich
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
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, in press
[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
vinecopulib: A C++ library for vine copulas
RcppThread: R-friendly threading in C++
wdm: Efficient implementation of weighted dependence measures and related independence tests
rvinecopulib: R interface to the vinecopulib C++ library
VineCopula: Statistical inference of vine copulas
wdm: R interface to the wdm C++ library
kde1d: Univariate kernel density estimators for bounded and discrete data
vinereg: D-vine copula quantile regression
VC2copula: Extend the ‘copula’ Package with Families and Models from ‘VineCopula’
kdecopula: Kernel smoothing for bivariate copula densities
kdevine: Multivariate kernel density estimation with vine copulas
cctols: Tools for continuous convolution in nonparametric estimation
jdify: Joint density classifiers
gamCopula: Generalized additive models for bivariate conditional dependence structures and vine copulas
Nonparametric statistical learning, TU Munich (Summer 2019)
Analysis for computer scientists, TU Munich (Winter 2018)
Stochastics for vocational school teachers, TU Munich (Summer 2018)
Computational statistics, TU Munich (Summer 2018)
Rank-based nonparametric statistics, TU Munich (Summer 2016)
Functional data analysis, TU Munich (Summer 2017)
Mathematical introduction to neural networks, TU Munich (Winter 2016)
Nonparametric statistical methods, TU Munich (Summer 2016)
Krüger, D. (2018). General vine copula models for stationary multivariate time series.
Weber, R. (2018). Value at Risk Estimation with
Subset Simulation.
Jebabli, E. (2018). Statistical methods for the objective assessment of subjective ratings of advanced driver assistance systems.
Kreuzer, A. (2016). Analysing the spatial dependency among fire danger indices.
Brauer, A. (2016). Kernel estimation of conditional copula densities.
Bumann, C. (2016). Sparse structure selection for high-dimensional vine copula models.
Annoh, I. (2016). Comparing two-part models for estimation of actuarial total loss.
Zhang, S. (2015). Copula-based total loss estimation with group effects on the dependence
structure.