Sebastian Fuchs

Position: Postdoc-Position - Leiter des Team "Dependence Modeling"

E-Mail: sebastian.fuchs@plus.ac.at

Website: Persönliche Webseite

Datum Beginn: 01.10.2019

Veröffentlichungen

J. Ansari, P. Langthaler, S. Fuchs, W. Trutschnig: Quantifying and estimating dependence via sensitivity of conditional distributions. (2026) https://doi.org/https://doi.org/10.3150/25-BEJ1854
S. Fuchs, C. Limbach, F. Schürrer: On exact regions between measures of concordance and Chatterjee’s rank correlation for lower semilinear copulas. (2025) https://doi.org/https://doi.org/10.1016/j.ijar.2025.109588
F. Durante, S. Fuchs, R. Pappadà: Clustering of compound events based on multivariate comonotonicity. (2025) https://doi.org/https://doi.org/10.1016/j.spasta.2025.100881
J. Ansari, S. Fuchs, W. Trutschnig, et al.: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. (2024) https://doi.org/https://doi.org/10.1007/978-3-031-65993-5
Y. Wang, S. Fuchs: Hierarchical Variable Clustering Based on Measures of Predictability. (2024) https://doi.org/https://doi.org/10.1007/978-3-031-65993-5_67
C. Limbach, S. Fuchs: Quantifying Directed Dependence with Kendall’s Tau. (2024) https://doi.org/https://doi.org/10.1007/978-3-031-65993-5_30
P. Langthaler, J. Ansari, S. Fuchs, W. Trutschnig: Constructing measures of dependence via sensitivity of conditional distributions. (2024) https://doi.org/ https://doi.org/10.1007/978-3-031-65993-5_28
S. Fuchs: Quantifying directed dependence via dimension reduction. (2024) https://doi.org/ https://doi.org/10.1016/j.jmva.2023.105266
S. Fuchs, Y. Wang: Hierarchical variable clustering based on the predictive strength between random vectors. (2024) https://doi.org/ https://doi.org/10.1016/j.ijar.2024.109185
T. Pollhammer, B. Salcher, S. Fuchs: MAMU: an R package for GIS-based river terrace mapping, morphostratigraphic evaluation of terrace maps and outcrop data and river long profile modelling. (2024) https://doi.org/https://doi.org/10.5194/egusphere-egu24-16766
S. Fuchs, M. Tschimpke: A novel positive dependence property and its impact on a popular class of concordance measures. (2024) https://doi.org/ https://doi.org/10.1016/j.jmva.2023.105259
S. Fuchs, M. Tschimpke: Total positivity of copulas from a Markov kernel perspective. (2022) https://doi.org/ https://doi.org/10.1016/j.jmaa.2022.126629
S. Fuchs: The simplifying assumption in pair-copula constructions from an analytic perspective. (2022) https://doi.org/ https://doi.org/10.1007/978-3-031-15509-3_20
S. Fuchs, M. Tschimpke: On positive dependence properties for Archimedean copulas. (2022) https://doi.org/https://doi.org/10.1007/978-3-031-15509-3_21
T. Mroz, J. Fernández Sánchez, S. Fuchs, W. Trutschnig: On distributions with fixed marginals maximizing the joint or the prior default probability, estimation, and related results. (2022) https://doi.org/https://doi.org/10.1016/j.jspi.2022.07.005
T. Kasper, S. Fuchs, W. Trutschnig: On weak conditional convergence of bivariate Archimedean and Extreme Value copulas, and consequences to nonparametric estimation. (2021) https://doi.org/https://doi.org/10.3150/20-BEJ1306
T. Kasper, S. Fuchs, W. Trutschnig: On convergence of associative copulas and related results. (2021) https://doi.org/https://doi.org/10.1515/demo-2021-0114
T. Mroz, S. Fuchs, W. Trutschnig: How simplifying and flexible is the simplifying assumption in pair-copula constructions – analytic answers in dimension three and a glimpse beyond. (2021) https://doi.org/https://doi.org/10.1214/21-EJS1832
S. Fuchs, F.M.L. Di Lascio and F. Durante: Dissimilarity functions for rank-based hierarchical clustering of continuous variables. (2021) https://doi.org/ https://doi.org/10.48550/arXiv.2007.04799
S. Fuchs, W. Trutschnig: On quantile-based co-risk measures and their estimation. (2020) https://doi.org/ https://doi.org/10.1515/demo-2020-0021
J.Y. Ahn, S. Fuchs, R. Oh: A copula transformation in multivariate mixed discrete-continuous models. (2020) https://doi.org/ https://doi.org/10.1016/j.fss.2020.11.008
S. Fuchs, K.D. Schmidt: On order statistics and Kendall’s tau for copulas. (2020) https://doi.org/https://doi.org/10.1016/j.spl.2020.108972