Tails of multivariate Archimedean copulas. - Arthur Charpentier; Johan Segers (J. Multivar. Anal., 2009)

On the covariance of the asymptotic empirical copula process. - Christian Genest; Johan Segers (J. Multivar. Anal., 2010)

Nonparametric estimation of an extreme-value copula in arbitrary dimensions. - Gordon Gudendorf; Johan Segers (J. Multivar. Anal., 2011)

Nonparametric estimation of the tree structure of a nested Archimedean copula. - Johan Segers; Nathan Uyttendaele (Comput. Stat. Data Anal., 2014)

Markov Tail Chains. - Anja Janßen; Johan Segers (J. Appl. Probab., 2014)

Detecting changes in cross-sectional dependence in multivariate time series. - Axel Bücher; Ivan Kojadinovic; Tom Rohmer; Johan Segers (J. Multivar. Anal., 2014)

Measuring association and dependence between random vectors. - Oliver Grothe; Julius Schnieders; Johan Segers (J. Multivar. Anal., 2014)

Nonparametric estimation of pair-copula constructions with the empirical pair-copula. - Ingrid Hobæk Haff; Johan Segers (Comput. Stat. Data Anal., 2015)

Marginal standardization of upper semicontinuous processes. With application to max-stable processes. - Anne Sabourin; Johan Segers (J. Appl. Probab., 2017)

The empirical beta copula. - Johan Segers; Masaaki Sibuya; Hideatsu Tsukahara (J. Multivar. Anal., 2017)

Gradient importance sampling: An efficient statistical extraction methodology of high-sigma SRAM dynamic characteristics. - Thomas Haine; Johan Segers; Denis Flandre; David Bol (DATE, 2018)

Bayesian inference for bivariate ranks. - Simon Guillotte; François Perron; Johan Segers (CoRR, 2018)

Weak convergence of the weighted empirical beta copula process. - Betina Berghaus; Johan Segers (J. Multivar. Anal., 2018)

On the weak convergence of the empirical conditional copula under a simplifying assumption. - François Portier; Johan Segers (J. Multivar. Anal., 2018)

Multivariate generalized Pareto distributions: Parametrizations, representations, and properties. - Holger Rootzén; Johan Segers; Jennifer L. Wadsworth (J. Multivar. Anal., 2018)

Monte Carlo integration with a growing number of control variates. - François Portier; Johan Segers (J. Appl. Probab., 2019)

Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions. - Anna Kiriliouk; Holger Rootzén; Johan Segers; Jennifer L. Wadsworth (Technometrics, 2019)

Risk bounds when learning infinitely many response functions by ordinary linear regression. - Vincent Plassier; François Portier; Johan Segers (CoRR, 2020)

Control variate selection for Monte Carlo integration. - Rémi Leluc; François Portier; Johan Segers (Stat. Comput., 2021)

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling. - Rémi Leluc; François Portier; Johan Segers; Aigerim Zhuman (NeurIPS, 2022)

Measuring dependence between random vectors via optimal transport. - Gilles Mordant; Johan Segers (J. Multivar. Anal., 2022)

Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence. - Rémi Leluc; François Portier; Johan Segers; Aigerim Zhuman (CoRR, 2023)

Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. - Rémi Leluc; Aymeric Dieuleveut; François Portier; Johan Segers; Aigerim Zhuman (ICML, 2024)

Wasserstein-Aitchison GAN for angular measures of multivariate extremes. - Stéphane Lhaut; Holger Rootzén; Johan Segers (CoRR, 2025)