| VineCopula: Statistical inference of vine copulas T Nagler, U Schepsmeier, J Stoeber, EC Brechmann, B Graeler, T Erhardt, ... R package version 2 (0), 2019 | 194* | 2019 |
| Generalized additive models for conditional dependence structures T Vatter, V Chavez-Demoulin Journal of Multivariate Analysis 141, 147-167, 2015 | 68 | 2015 |
| rvinecopulib: High performance algorithms for vine copula modeling T Nagler, T Vatter R package version 3, 2021 | 60* | 2021 |
| Generalized additive models for pair-copula constructions T Vatter, T Nagler Journal of Computational and Graphical Statistics 27 (4), 715-727, 2018 | 48 | 2018 |
| Deep smoothing of the implied volatility surface D Ackerer, N Tagasovska, T Vatter Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020 | 45 | 2020 |
| Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery N Tagasovska, V Chavez-Demoulin, T Vatter International Conference on Machine Learning, 9311-9323, 2020 | 40* | 2020 |
| Copulas as high-dimensional generative models: Vine copula autoencoders N Tagasovska, D Ackerer, T Vatter Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 39 | 2019 |
| Generative models for simulating mobility trajectories V Kulkarni, N Tagasovska, T Vatter, B Garbinato Workshop on Modeling and Decision-Making in the Spatiotemporal Domain …, 2018 | 36 | 2018 |
| Kde1d: Univariate kernel density estimation T Nagler, T Vatter R package version 1 (2), 6, 2019 | 24 | 2019 |
| gamCopula: Generalized additive models for bivariate conditional dependence structures and vine copulas T Vatter, T Nagler R package, 0.0-4, 2017 | 16 | 2017 |
| VineCopula: Statistical Inference of Vine Copulas, R Package Version 2.4. 2 T Nagler, U Schepsmeier, J Stoeber, EC Brechmann, B Graeler, T Erhardt, ... R Project, 2021 | 15 | 2021 |
| Solving estimating equations with copulas T Nagler, T Vatter Journal of the American Statistical Association, 1-13, 2023 | 10 | 2023 |
| Package “VineCopula’, R package version, 2 U Schepsmeier, J Stoeber, EC Brechmann, B Graeler, T Nagler, T Erhardt, ... | 9 | 2015 |
| Non-parametric estimation of intraday spot volatility: disentangling instantaneous trend and seasonality T Vatter, HT Wu, V Chavez-Demoulin, B Yu Econometrics 3 (4), 864-887, 2015 | 8 | 2015 |
| Package ‘VineCopula’ T Nagler, U Schepsmeier, J Stoeber, EC Brechmann, B Graeler, T Erhardt, ... | 7 | 2023 |
| Generalized additive models for conditional copulas V Chavez-Demoulin, T Vatter J. Multivariate Anal 141, 147-167, 2015 | 6 | 2015 |
| kde1d: Univariate Kernel Density Estimation, 2018 T Nagler, T Vatter R package version 0.2 1, 0 | 6 | |
| Marginalizable density models D Gilboa, A Pakman, T Vatter arXiv preprint arXiv:2106.04741, 2021 | 5 | 2021 |
| Dependent defaults and losses with factor copula models D Ackerer, T Vatter Dependence Modeling 5 (1), 375-399, 2017 | 5 | 2017 |
| Generalized Additive Modeling For Multivariate Distributions T Vatter Université de Lausanne, Faculté des hautes études commerciales, 2016 | 1 | 2016 |
Thomas NaglerLMU Munich, Munich Center for Machine LearningVerified email at lmu.de
