Submitted
[-] Fuchs, S. and Y. Wang. An mtp2 property for conditional distributions.
[-] Ansari, J. and S. Fuchs. An ordering for the strength of functional dependence. arxiv
[-] Fuchs, S., P. B. Langthaler, and C. Limbach. A new coefficient of separation. arxiv
[-] Ansari, J. and S. Fuchs. A direct extension of Azadkia & Chatterjee's rank correlation to multi-response vectors. arxiv
Peer-Reviewed Journal Publications
[33] Ansari, J. and S. Fuchs. On continuity of Chatterjee's rank correlation and related dependence measures. Bernoulli, to appear. arxiv
[32] Fuchs, S., K.D. Schmidt, and Y. Wang (2026). A note on Bertino and Fredricks-Nelsen copulas. Fuzzy Sets and Systems 534, Article ID 109846. doi.org/10.1016/j.fss.2026.109846
[31] Fuchs, S. and C. Limbach (2026). A dimension reduction for extreme types of directed dependence. Dependence Modeling 14, Article ID 20250016. arxiv doi.org/10.1515/demo-2025-0016
[30] Salcher, B. C., S. Neuhuber, J.-C. Otto, T. Payer, C. Lüthgens, S. Fuchs, A. Flores-Orozco, J. Norgard, S. Grupe, O. Marchhart, A. Wieser, J. Lachner, M. Fiebig, Z. Ruszkiczay-Rüdiger (2026). Quaternary terrace formation in response to climate, regional uplift and local normal faulting: The Danube terrace staircase of Vienna. Quaternary Science Reviews 373, Article ID 109709. doi.org/10.1016/j.quascirev.2025.109709
[29] Fuchs, S., C. Limbach and F. Schürrer (2026). On exact regions between measures of concordance and Chatterjee's rank correlation for lower semilinear copulas. International Journal of Approximate Reasoning, 189, Article ID 109588. arxiv doi.org/10.1016/j.ijar.2025.109588
[28] Durante, F., S. Fuchs, and R. Pappadà (2025). Clustering of compound events based on multivariate comonotonicity. Spatial Statistics 66, Article ID 100881. doi.org/10.1016/j.spasta.2025.100881
[27] Ansari, J., P. B. Langthaler, S. Fuchs, and W. Trutschnig (2026). Quantifying and estimating dependence via sensitivity of conditional distributions. Bernoulli 32, 179-204 . doi 10.3150/25-BEJ1854. arxiv doi.org/10.3150/25-BEJ1854
[26] Fuchs, S. and Y. Wang (2024). Hierarchical variable clustering based on the predictive strength between random vectors. International Journal of Approximate Reasoning 170, Article ID 109185. arxiv doi.org/10.1016/j.ijar.2024.109185
[25] Fuchs, S. and M. Tschimpke (2024). A novel positive dependence property and its impact on a popular class of concordance measures. Journal of Multivariate Analysis 200, Article ID 105259. arxiv doi.org/10.1016/j.jmva.2023.105259
[24] Fuchs, S. (2024). Quantifying directed dependence via dimension reduction. Journal of Multivariate Analysis 201, Article ID 105266. arxiv doi.org/10.1016/j.jmva.2023.105266
[23] Fuchs, S. and M. Tschimpke (2023). Total positivity of copulas from a Markov kernel perspective. Journal of Mathematical Analysis and Applications 518, Article ID 126629. arxiv doi.org/10.1016/j.jmaa.2022.126629
[22] Mroz, T., J. Fernández-Sánchez, S. Fuchs, and W. Trutschnig (2023). On distributions with fixed marginals maximizing the joint or the prior default probability, estimation, and related results. Journal of Statistical Planning and Inference 223, 33-52. doi.org/10.1016/j.jspi.2022.07.005
[21] Kasper, T., S. Fuchs, and W. Trutschnig (2021). On convergence of associative copulas and related results. Dependence Modeling 9, 307-326. doi.org/10.1515/demo-2021-0114
[20] Mroz, T., S. Fuchs, and W. Trutschnig (2021). How simplifying and flexible is the simplifying assumption in pair-copula constructions - analytic answers in dimension three and a glimpse beyond. Electronic Journal of Statistics 15 (1), 1951-1992. arxiv doi.org/10.1214/21-EJS1832
[19] Fuchs, S., F.M.L. Di Lascio, and F. Durante (2021). Dissimilarity functions for rank-based hierarchical clustering of continuous variables. Computational Statistics & Data Analysis 159, Article ID 107201, 26 pages. arxiv doi.org/10.1016/j.csda.2021.107201
[18] Kasper, T., S. Fuchs, and W. Trutschnig (2021). On weak conditional convergence of bivariate Archimedean and extreme value copulas, and consequences to nonparametric estimation. Bernoulli 27 (4), 2217-2240. arxiv doi.org/10.3150/20-BEJ1306
[17] Fuchs, S. and W. Trutschnig (2020). On quantile based co-risk measures and their estimation. Dependence Modeling 8, 396-41 doi.org/10.1515/demo-2020-0021
[16] Ahn, J.Y., S. Fuchs, and R. Oh (2021). A copula transformation in multivariate mixed discrete-continuous models. Fuzzy Sets and Systems. 415, 54-75. doi.org/10.1016/j.fss.2020.11.008
[15] Fuchs, S. and K.D. Schmidt (2021). On order statistics and Kendall's tau. Statistics and Probability Letters 169, Article ID 108972, 7 pages. doi.org/10.1016/j.spl.2020.108972
[14] Ahn, J.Y. and S. Fuchs (2020). On minimal copulas under the concordance order. Journal of Optimization Theory and Applications 184, 762-780. doi.org/10.1007/s10957-019-01618-4
[13] Mutwill, A.M., T.D. Zimmermann, C. Reuland, S. Fuchs, J. Kunert, S.H. Richter, S. Kaiser, and N. Sachser (2019). High reproductive success despite queuing – Socio-sexual development of males in a complex social environment. Frontiers in Psychology 10, no. 2810, 9 pages. doi.org/10.3389/fpsyg.2019.02810
[12] Fuchs, S. and Y. McCord (2019). On the lower bound of Spearman's footrule. Dependence Modeling 7, 121-129. doi.org/10.1515/demo-2019-0005
[11] Durante, F. and S. Fuchs (2019). Reflection invariant copulas. Fuzzy Sets and Systems 354 (1), 63-73. doi.org/10.1016/j.fss.2018.02.004
[10] Fuchs, S. (2018). Conditioning of copulas: Transformations, invariance and measures of concordance. Journal of Mathematical Analysis and Applications 462 (1), 521-541. doi.org/10.1016/j.jmaa.2018.02.014
[9] Fuchs, S., Y. McCord, and K.D. Schmidt (2018). Characterizations of copulas attaining the bounds of multivariate Kendall's tau. Journal of Optimization Theory and Applications 178 (2), 424-438. doi.org/10.1007/s10957-018-1285-6
[8] Fuchs, S., R. Schlotter, and K.D. Schmidt (2017). A review and some complements on quantile risk measures and their domain. Risks 5 (4), Article ID 59, 16 pages. doi.org/10.3390/risks5040059
[7] Dietz, M., S. Fuchs, and K.D. Schmidt (2016). On order statistics and their copulas. Statistics and Probability Letters 117, 165-172. doi.org/10.1016/j.spl.2016.05.020
[6] Fuchs, S. (2016). Copula-induced measures of concordance. Dependence Modeling 4, 205-214. doi.org/10.1515/demo-2016-0011
[5] Fuchs, S. (2016). A biconvex form for copulas. Dependence Modeling 4, 63-75. doi.org/10.1515/demo-2016-0003
[4] Fuchs, S. (2014). Multivariate copulas: Transformations, symmetry, order and measures of concordance. Kybernetika 50 (5), 725-743. doi.org/10.14736/kyb-2014-5-0725
[3] Fuchs, S. and K.D. Schmidt (2014). Bivariate copulas: Transformations, asymmetry and measures of concordance. Kybernetika 50 (1), 109-125. doi.org/10.14736/kyb-2014-1-0109
[2] Fuchs, S. (2014). Consistent loss prediction for a portfolio and its subportfolios. Scandinavian Actuarial Journal 2014 (6), 561-581. doi.org/10.1080/03461238.2012.749508
[1] Fuchs, S., A. Ludwig, and K.D. Schmidt (2013). Zur Exaktheit der Standardformel. Zeitschrift für die Gesamte Versicherungswirtschaft 102 (1), 87-95. doi.org/10.1007/s12297-012-0223-1
Books, Edited Books, Proceedings, Special Issues
[2] S. Fuchs (2026). Special issue on the 11th International Conference on Soft Methods in Probability and Statistics (SMPS 2024). International Journal of Approximate Reasoning, 188, Article ID 109584.
[1] Ansari, J., S. Fuchs, W. Trutschnig, M.A. Lubiano, M.A. Gil, P. Grzegorzewski, and O. Hryniewicz (2024) Combining, Modelling and Analyzing Imprecission, Randomness and Dependence. SMPS 2024 Conference proceedings. Springer, Cham.
R Packages
[2] Wang, Y., S. Fuchs and J. Ansari (2024). didec: Directed Dependence Coefficient. R package version 1.1.0. CRAN.R-project.org/package=didec
[1] Pollhammer, T., B. Salcher, S. Fuchs (2024). MAMU: an R package for GIS-based river terrace mapping, morphostratigraphic evaluation of terrace maps and outcrop data and river long profile modelling. EGU General Assembly 2024, Vienna, Austria, EGU24-16766.
Book chapters
[6] Wang, Y. and S. Fuchs. (2024). Hierarchical variable clustering based on measures of predictability. In J. Ansari et al. (Eds.), Combining, Modelling and Analyzing Imprecission, Randomness and Dependence, pp. 559–564. Springer, Cham.
[5] Limbach, C. and S. Fuchs. (2024). Quantifying directed dependence with Kendall's tau. In J. Ansari et al. (Eds.), Combining, Modelling and Analyzing Imprecission, Randomness and Dependence, pp. 257–264. Springer, Cham.
[4] Langthaler, P. B., J. Ansari, S. Fuchs, and W. Trutschnig. (2024). Constructing measures of dependence via sensitivity of conditional distributions. In J. Ansari et al. (Eds.), Combining, Modelling and Analyzing Imprecission, Randomness and Dependence, pp. 241–248. Springer, Cham.
[3] Fuchs, S. (2022). The simplifying assumption in pair-copula constructions from an analytic perspective. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science, pp. 151–158. Springer, Cham.
[2] Fuchs, S. and M. Tschimpke (2022). On positive dependence properties for Archimedean copulas. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science, pp. 159–165. Springer, Cham.
[1] Fuchs, S., H.J. Klemmt, and K.D. Schmidt (2012). Aggregation. In M. Radtke and K.D. Schmidt (Eds.), Handbuch zur Schadenreservierung, pp. 37–44. Verlag Versicherungswirtschaft, Karlsruhe.
Theses
Fuchs, S. (2022). Recent Developments in Dependence Modeling and Copulas - Quantifying dependence in multivariate stochastic models. Habilitation thesis, Paris-Lodron Universität Salzburg, Austria.
Fuchs, S. (2015). Transformations of Copulas and Measures of Concordance. PhD thesis, Technische Universität Dresden, Germany.