| Nonparametric C-and D-vine-based quantile regression M Tepegjozova, J Zhou, G Claeskens, C Czado Dependence Modeling 10 (1), 1-21, 2022 | 14 | 2022 |
| D-and C-vine quantile regression for large data sets M Tepegjozova | 8 | 2019 |
| Bivariate vine copula based quantile regression M Tepegjozova, C Czado Preprint, 2022 | 2 | 2022 |
| Statistical learning with vine copulas in regression settings M Tepegjozova Technische Universität München, 2023 | 1 | 2023 |
| Bivariate vine copula based regression, bivariate level and quantile curves M Tepegjozova, C Czado arXiv preprint arXiv:2205.02557, 2022 | 1 | 2022 |
| Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria M Tepegjozova, BF Meyer, A Rammig, CS Zang, C Czado arXiv preprint arXiv:2310.10324, 2023 | | 2023 |
| Quantifying climate change induced shifts in the risk of jointly and individually occurring drought and late-spring frost BF Meyer, M Tepegjozova, A Rammig, C Czado, CS Zang EGU General Assembly Conference Abstracts, EGU-15290, 2023 | | 2023 |
| Nonparametric C-and D-vine based quantile regression M Tepegjozova, J Zhou, G Claeskens, C Czado Dependence Modeling, 2021 | | 2021 |
| Nonparametric C-and D-vine based quantile regression M Tepegjozova, J Zhou, G Claeskens, C Czado Dependence Modeling, 2021 | | 2021 |
Claudia CzadoAssociate Professor for Applied Mathematical StatisticsVerified email at ma.tum.de
