Peer-Reviewed Journal Publications
[28] Durante, F., S. Fuchs, and R. Pappadà (2025). Clustering of compound events based on multivariate comonotonicity. Spatial Statistics 66, Article ID 100881.
[27] Ansari, J., P. B. Langthaler, S. Fuchs, and W. Trutschnig (2025). Quantifying and estimating dependence via sensitivity of conditional distributions. Bernoulli, to appear. arxiv
[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
[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
[24] Fuchs, S. (2024). Quantifying directed dependence via dimension reduction. Journal of Multivariate Analysis 201, Article ID 105266. arxiv
[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
[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.
[21] Kasper, T., S. Fuchs, and W. Trutschnig (2021). On convergence of associative copulas and related results. Dependence Modeling 9, 307-326.
[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.
[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.
[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.
[17] Fuchs, S. and W. Trutschnig (2020). On quantile based co-risk measures and their estimation. Dependence Modeling 8, 396-416.
[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.
[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.
[14] Ahn, J.Y. and S. Fuchs (2020). On minimal copulas under the concordance order. Journal of Optimization Theory and Applications 184, 762-780.
[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.
[12] Fuchs, S. and Y. McCord (2019). On the lower bound of Spearman's footrule. Dependence Modeling 7, 121-129.
[11] Durante, F. and S. Fuchs (2019). Reflection invariant copulas. Fuzzy Sets and Systems 354 (1), 63-73.
[10] Fuchs, S. (2018). Conditioning of copulas: Transformations, invariance and measures of concordance. Journal of Mathematical Analysis and Applications 462 (1), 521-541.
[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.
[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.
[7] Dietz, M., S. Fuchs, and K.D. Schmidt (2016). On order statistics and their copulas. Statistics and Probability Letters 117, 165-172.
[6] Fuchs, S. (2016). Copula-induced measures of concordance. Dependence Modeling 4, 205-214.
[5] Fuchs, S. (2016). A biconvex form for copulas. Dependence Modeling 4, 63-75.
[4] Fuchs, S. (2014). Multivariate copulas: Transformations, symmetry, order and measures of concordance. Kybernetika 50 (5), 725-743.
[3] Fuchs, S. and K.D. Schmidt (2014). Bivariate copulas: Transformations, asymmetry and measures of concordance. Kybernetika 50 (1), 109-125.
[2] Fuchs, S. (2014). Consistent loss prediction for a portfolio and its subportfolios. Scandinavian Actuarial Journal 2014 (6), 561-581.
[1] Fuchs, S., A. Ludwig, and K.D. Schmidt (2013). Zur Exaktheit der Standardformel. Zeitschrift für die Gesamte Versicherungswirtschaft 102 (1), 87-95.