KU Leuven and UCLouvain

To download preprints and discussion papers, see my publication lists on arXiv, ResearchGate or Google Scholar.

ORCID: https://orcid.org/0000-0002-0444-689X

Book

  • Beirlant, J., Goegebeur, Y., Segers, J. and Teugels, J. (2004) Statistics of Extremes: Theory and Applications, Wiley Series in Probability and Statistics, John Wiley & Sons Ltd., Chichester.

Articles in peer-reviewed academic journals

  • Allen, S., Koh, J., Segers, J. and Ziegel, J. (2025) “Tail calibration of probabilistic forecasts”, Journal of the American Statistical Association, forthcoming.
  • Kirch, C., Lahiri, S., Binder, H., Brannath, W., Cribben, I., Detter, H., Doebler, P., Fenq, A., Greven, S., Hammer, B., Harmelinq, S., Hotz, T., Kauermann, G., Krause, J., Krempl, G., Nieto-Reyes, A., Okhrin, O., Ombao, H., Pein, F., Pesta, M., Politis, D., Qin, L., Rainforth, T., Rauhut, H., Reeve, H., Salinas, D., Schmidt-Hieber, J., Scott, C., Segers, J., Spiliopoulou, M., Wilhelm, A., Wilms, I., Yu, Y. and Lederer, J. (2025) “Challenges and Opportunities for Statistics in the Era of Data Science”, Harvard Data Science Review 7(2), .
  • Leluc, R., Portier, F., Segers, J. and Zhuman, A. (2025) “Speeding up Monte Carlo integration: control neighbors for optimal convergence”, Bernoulli 31(2), 1160–1180.
  • Asenova, S. and Segers, J. (2024) “Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments”, Advances in Applied Probability, forthcoming.
  • Hentschel, M., Englelke, S. and Segers, J. (2024) “Statistical inference for Hüsler–Reiss graphical models through matrix completions”, Journal of the American Statistical Association 120(550), 909--921.
  • Hu, S., Peng, Z. and Segers, J. (2024) “Modeling multivariate extreme value distributions via Markov trees”, Scandinavian Journal of Statistics 51(2), 760–800.
  • Kiriliouk, A., Lee, J. and Segers, J. (2024) “X-vine models for multivariate extremes”, Journal of the Royal Statistical Society, Series B (Statistical Methodology), qkae105.
  • Leluc, R., Dieuleveut, A., Portier, F., Segers, J. and Zhuman, A. (2024) “Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates”, International Conference in Machine Learning, conference poster.
  • Mourahib, A., Kiriliouk, A. and Segers, J. (2024) “Multivariate generalized Pareto distributions along extreme directions”, Extremes 28, 239–272.
  • Asenova, S. and Segers, J. (2023) “Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler–Reiss distributions”, Extremes 26, 433–468.
  • Clémençon, S., Jalalzai, H., Lhaut, S., Sabourin, A. and Segers, J. (2023) “Concentration bounds for the empirical angular measure with statistical learning applications”, Bernoulli 29(4), 2797–2827.
  • Janßen, A. and Segers, J. (2023) “Invariance properties of limiting point processes and applications to clusters of extremes”, Dependence Modeling 12, 20230109.
  • Oorschot, J., Segers, J. and Zhou, C. (2023) “Tail inference using extreme U-statistics”, Electronic Journal of Statistics 17(1), 1113–1159.
  • Plassier, V., Portier, F. and Segers, J. (2023) “Risk bounds when learning infinitely many response functions by ordinary linear regression”, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 59(1), 53–78.
  • Leluc, R., Portier, F., Segers, J. and Zhuman, A. (2022) “A quadrature rule combining control variates and adaptive importance sampling”, Advances in Neural Information Processing Systems 35, 11842–11853.
  • Lhaut, S., Sabourin, A. and Segers, J. (2022) “Uniform concentration bounds for frequencies of rare events”, Statistics & Probability Letters 189, 109610.
  • Mordant, G. and Segers, J. (2022) “Measuring dependence between random vectors via optimal transport”, Journal of Multivariate Analysis, 104912.
  • Asenova, S., Mazo, G. and Segers, J. (2021) “Inference on extremal dependence in a latent Markov tree model attracted to a Hüsler–Reiss distribution”, Extremes 24, 461–500.
  • Einmahl, J.H. and Segers, J. (2021) “Empirical tail copulas for functional data”, The Annals of Statistics, forthcoming.
  • Leluc, R., Portier, F. and Segers, J. (2021) “Control variate selection for Monte Carlo integration”, Statistics and Computing, forthcoming.
  • Mordant, G. and Segers, J. (2021) “Maxima and near-maxima of a Gaussian random assignment field”, Statistics & Probability Letters 173, forthcoming.
  • Hallin, M., Mordant, G. and Segers, J. (2021) “Multivariate goodness-of-fit tests based on Wasserstein distance”, Electronic Journal of Statistics 15(1), 1328–1371.
  • Segers, J. (2020) “One- versus multi-component regular variation and extremes of Markov trees”, Advances in Applied Probability 52(3), 855–878.
  • Vettori, S., Huser, R., Segers, J. and Genton, M.G. (2020) “Bayesian model averaging over tree-based dependence structures for multivariate extremes”, Journal of Computational and Graphical Statistics 20(1), 174–190.
  • Portier, F. and Segers, J. (2019) “Monte Carlo integration with a growing number of control variates”, Journal of Applied Probability  56, 1168–1186.
  • Asmussen, S., Ivanovs, J. and Segers, J. (2019) “On the longest gap between power-rate arrivals”, Bernoulli  25, 375–394.
  • Chiapino, M., Sabourin, A. and Segers, J. (2019) “Identifying groups of variables with the potential of being large simultaneously”, Extremes  22, 193–222.
  • Kiriliouk, A., Rootzén, H., Wadsworth, J.L. and Segers, J. (2019) “Peaks-Over-Thresholds Modeling with Multivariate Generalized Pareto Distribtions”, Technometrics  61, 123–135.
  • Berghaus, B. and Segers, J. (2018) “Weak convergence of the weighted empirical beta copula process”, Journal of Multivariate Analysis  166, 266–281.
  • Bücher, A. and Segers, J. (2018) “Inference for heavy tailed stationary time series based on sliding blocks”, Electronic Journal of Statistics  12, 1098–1125.
  • Bücher, A. and , Segers, J. (2018) “Maximum likelihood estimation for the Fréchet distribution based on block maxima extracted from a time series”, Bernoulli  24, 1427–1462.
  • Davis, R., Drees, H., Segers, J. and Warchoł, M. (2018) “Inference on the tail process with application to financial time series modelling”, Journal of Econometrics  205, 508–525.
  • Einmahl, J.H., Kiriliouk, A. and Segers, J. (2018) “A continuous updating weighted least squares estimator of tail dependence in high dimensions”, Extremes  21, 205–233.
  • Haine, H., Segers, J., Flandre, D. and Bol, D. (2018) “Gradient Importance Sampling: an Efficient Statistical Extraction Methodology of High-Sigma SRAM Dynamic Characteristics”, Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018  2018-January, 195–200.
  • Kiriliouk, A., Segers, J. and Tafakori, L. (2018) “An estimator of the stable tail dependence function based on the empirical beta copula”, Extremes  21, 581–600.
  • Portier, F. and Segers, J. (2018) “On the weak convergence of the empirical conditional copula under a simplifying assumption”, Journal of Multivariate Analysis  166, 160–181.
  • Rootzén, H., Segers, J. and Wadsworth, J.L. (2018) “Multivariate peaks over thresholds models”, Extremes  21, 115–145.
  • Rootzén, H., Segers, J. and Wadsworth, J.L. (2018) “Multivariate generalized Pareto distributions: parametrizations, representations, and properties”, Journal of Multivariate Analysis  165, 117–131.
  • van Loenhout, J.A.F., Delbiso, T.D., Kiriliouk, A., Rodriguez-Llanes, J.M., Segers, J. and Guha-Sapir, D. (2018) “Heat and emergency room admissions in the Netherlands”, BMC Public Health  18, 108.
  • Bücher, A. and Segers, J. (2017) “On the maximum likelihood estimator for the generalized extreme-value distribution”, Extremes  20, 839–872.
  • Padoan, S., Marcon, G., Naveau, P., Muliere, P. and Segers, J. (2017) “Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials”, Journal of Statistical Planning and Inference  183, 1–17.
  • Sabourin, A. and Segers, J. (2017) “Marginal standardization of upper semicontinuous processes. With application to max-stable processes”, Journal of Applied Probability  54, 773–796.
  • Segers, J., Sibuya, M. and Tsukahara, H. (2017) “The empirical beta copula”, Journal of Multivariate Analysis  155, 35–51.
  • Segers, J., Zhao, Y. and Meinguet, T. (2017) “Polar decomposition of regularly varying time series in star-shaped metric spaces”, Extremes  20, 539–566.
  • Padoan, S., Marcon, G., Naveau, P., Muliere, P. and Segers, J. (2017) “Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials”, Journal of Statistical Planning and Inference  183, 1–17.
  • Einmahl, J., Kiriliouk, A., Krajina, A. and Segers, J. (2016) “An M-estimator of spatial tail dependence”, Journal of the Royal Statistical Society, Series B (Statistical Methodology) 78, 275–298.
  • Denuit, M., Kiriliouk, A. and Segers, J. (2015) “Max-factor individual risk models with application to credit portfolios”, Insurance: Mathematics and Economics  62, 162–172.
  • Drees, H., Segers, J. and Warchoł, M. (2015) “Statistics for tail processes of Markov chains”, Extremes 18, 369–402.
  • Hobaek Haff, I. and Segers, J. (2015) “Nonparametric estimation of pair-copula constructions with the empirical pair-copula”, Computational Statistics and Data Analysis  84, 1–13.
  • Segers, J. (2015) “Hybrid copula estimators”, Journal of Statistical Planning and Inference  160, 23–34.
  • Bücher, A., Segers, J. and Volgushev, S. (2014) “When uniform weak convergence fails: empirical processes for dependence functions and residuals via epi- and hypographs”, The Annals of Statistics  42, 1598–1634.
  • Bücher, A. and Segers, J. (2014) “Extreme value copula estimation based on block maxima of a multivariate stationary time series”, Extremes  17, 495–528.
  • Bücher, A., Kojadinovic, I., Rohmer, T. and Segers, J. (2014) “Detecting changes in cross-sectional dependence in multivariate time series”, Journal of Multivariate Analysis  132, 111–128.
  • Grothe, O., Schnieders, J. and Segers, J. (2014) “Measuring association and dependence between random vectors”, Journal of Multivariate Analysis  123, 96–110.
  • Janssen, A. and Segers, J. (2014) “Markov tail chains”, Journal of Applied Probability  51, 1133–1153.
  • Segers, J. and Uyttendaele, N. (2014) “Nonparametric estimation of the tree structure of a nested Archimedean copula”, Computational Statistics and Data Analysis  72, 190–204.
  • Segers, J., van den Akker, R. and Werker, B.J. (2014) “Semiparametric Gaussian copula models: Geometry and rank-based efficient estimation”, The Annals of Statistics  42, 1911–1940.
  • De Carvalho, M., Oumow, B., Segers, J. and Warchoł, M. (2013) “A Euclidean Likelihood Estimator for Bivariate Tail Dependence”, Communications in Statistics - Theory and Methods  42, 1176–1192.
  • Basrak, B., Krizmanic, D. and Segers, J. (2012) “A functional limit theorem for dependent sequences with infinite variance stable limits”, The Annals of Probability  40, 2008–2033.
  • Einmahl, J.H.J., Krajina, A. and Segers, J. (2012) “An M-estimator for tail dependence in arbitrary dimensions”, The Annals of Statistics  40, 1764–1793.
  • Gudendorf, G. and Segers, J. (2012) “Nonparametric estimation of multivariate extreme-value copulas”, Journal of Statistical Planning and Inference  142, 3073–3085.
  • Segers, J. (2012) “Asymptotics of empirical copula processes under nonrestrictive smoothness assumptions”, Bernoulli  18, 764–782.
  • Segers, J. (2012) “Max-stable models for extremal dependence”, RevStat Statistical Journal  10, 61–82.
  • Gudendorf, G. and Segers, J. (2011) “Nonparametric estimation of an extreme-value copula in arbitrary dimensions”, Journal of Multivariate Analysis  102, 37–47.
  • Guillotte, S., Perron, F. and Segers, J. (2011) “Non-parametric Bayesian inference on bivariate extremes”, Journal of the Royal Statistical Society, Series B (Statistical Methodology)  73, 377–406.
  • Kojadinovic, I., Segers, J. and Yan, J. (2011) “Large-sample tests of extreme-value dependence for multivariate copulas”, The Canadian Journal of Statistics  39, 703–720.
  • Manner, H. and Segers, J. (2011) “Tails of correlation mixtures of elliptical copulas”, Insurance: Mathematics and Economics  48, 153–160.
  • Degen, M., Lambrigger, D.D. and Segers, J. (2010) “Risk concentration and diversification: second-order properties”, Insurance: Mathematics and Economics  46, 541–546.
  • Genest, C. and Segers, J. (2010) “On the asymptotic covariance of the empirical copula process”, Journal of Multivariate Analysis  101, 1837–1845.
  • Omey, E. and Segers, J. (2010) “Generalised regular variation of arbitrary order”, Banach Center Publications  90, 111–137.
  • Basrak, B. and Segers, J. (2009) “Regularly varying multivariate time series”, Stochastic Processes and Their Applications  119, 1055–1080.
  • Beirlant, J., Joossens, E. and Segers, J. (2009) “Second-order refined peaks-over-threshold modelling for heavy-tailed distributions”, Journal of Statistical Planning and Inference  139, 2800–2815.
  • Charpentier, A. and Segers, J. (2009) “Tails of multivariate Archimedean copulas”, Journal of Multivariate Analysis  100, 1521–1537.
  • Einmahl, J.H.J. and Segers, J. (2009) “Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution”, The Annals of Statistics  37, 2953–2989.
  • Ferro, C.A.T., Robert, C.Y. and Segers, J. (2009) “A sliding blocks estimator for the extremal index”, Electronic Journal of Statistics  3, 993–1020.
  • Genest, C. and Segers, J. (2009) “Rank-based inference for bivariate extreme-value copulas”, The Annals of Statistics  37, 2990–3022.
  • Charpentier, A. and Segers, J. (2008) “Convergence of Archimedean copulas”, Statistics & Probability Letters  78, 412–419.
  • Einmahl, J.H.J., Krajina, A. and Segers, J. (2008) “A method of moments estimator of tail dependence”, Bernoulli  14, 1003–1026.
  • Fils-Villetard, A., Guillou, A. and Segers, J. (2008) “Projection estimators of Pickands dependence functions”, The Canadian Journal of Statistics  36, 369–382.
  • Robert, C.Y. and Segers, J. (2008) “Tails of random sums of a heavy-tailed number of light-tailed terms”, Insurance: Mathematics and Economics  43, 85–92.
  • Charpentier, A. and Segers, J. (2007) “Lower tail dependence for Archimedean copulas: characterizations and pitfalls”, Insurance: Mathematics and Economics  40, 525–532.
  • Haeusler, E. and Segers, J. (2007) “Assessing confidence intervals for the tail index by Edgeworth expansions for the Hill estimator”, Bernoulli  13, 175–194.
  • Roberts, G.O., Rosenthal, J.S., Segers, J. and Sousa, B. (2006) “Extremal indices, geometric ergodicity of Markov chains, and MCMC”, Extremes  9, 213–229.
  • Segers, J. (2006) “Rare events, temporal dependence, and the extremal index”, Journal of Applied Probability  43, 463–485.
  • Segers, J. (2005) “Approximate distributions of clusters of extremes”, Statistics & Probability Letters  74, 330–336.
  • Segers, J. (2005) “Generalized Pickands estimators for the extreme value index”, Journal of Statistical Planning and Inference  128, 381–396.
  • Ferro, C.A.T. and Segers, J. (2003) “Inference for clusters of extreme values”, Journal of the Royal Statistical Society, Series B (Statistical Methodology)  65, 545–556.
  • Segers, J. (2003) “Functionals of clusters of extremes”, Advances in Applied Probability  35, 1028–1045.
  • Segers, J. (2002) “Abelian and Tauberian theorems on the bias of the Hill estimator”, Scandinavian Journal of Statistics  29, 461–483.
  • Segers, J. (2001) “On the normal uniform local domain of attraction for intermediate order statistics”, Statistics & Probability Letters  53, 409–413.
  • Segers, J. (2001) “Residual estimators”, Journal of Statistical Planning and Inference  98, 15–27.
  • Segers, J. and Teugels, J. (2000) “Testing the Gumbel hypothesis by Galton's ratio”, Extremes  3, 291–303.

Book chapters

  • Naveau, P. and Segers, J. (2025) “Multivariate extreme value theory”, in: Handbook on Statistics of Extremes (de Carvalho, M., Huser, R., Naveau, P. and Reich, B.J., eds.) Chapman and Hall/CRC, , forthcoming.
  • Lhaut, S. and Segers, J. (2024) “An asymptotic expansion of the empirical angular measure for bivariate extremal dependence”, in: Recent Advances in Econometrics and Statistics. Festschrift in Honour of Marc Hallin (Barigozzi, M., Hörmann, S. and Paindaveine, D., eds.) Springer Nature, Cham, 187–208.
  • Kiriliouk, A., Segers, J. and Tsukahara, H. (2021) “Resampling procedures with empirical beta copulas”, in: Pioneering Works on Extreme Value Theory: In Honor of Masaaki Sibuya (Hoshino, N., Mano, S. and Shimura, T., eds.) Springer Nature, Singapore, 27–53.
  • Kiriliouk, A., Segers, J. and Warchoł, M. (2016) “Nonparametric Estimation of Extremal Dependence”, in: Extreme Value Modeling and Risk Analysis. Methods and Applications (Dey, D.K. and Yan, J., eds.) CRC Press, Taylor & Francis Group, LLC, Boca Raton, FL, 353–376.
  • Gudendorf, G. and Segers, J. (2010) “Extreme-value copulas”, in: Copula theory and its applications (Warsaw, 2009) (Jaworski, P., Durante, F., Härdle, W. and Rychlik, W., eds.) Lecture Notes in Statistics – Proceedings, Springer-Verlag, Berlin, 127–146.
  • Segers, J. (2007) “Non-parametric inference for bivariate extreme-value copulas”, in: Topics in Extreme Values (Ahsanullah, M. and Kirmani, S.N.U.A., eds.) Nova Science Publishers Inc, New York, 181–203.

PhD thesis

  • Segers, J., Extremes of a Random Sample: Limit Theorems and Applications, Katholieke Universiteit Leuven, 2001-05-09, supervisor: Jozef L. Teugels.

Contributions to articles with discussion

  • Segers, J. (2020) “Discussion of « Graphical models for extremes » by S. Engelke and A.S. Hitz”, Journal of the Royal Statistical Society, Series B (Statistical Methodology) 82(4), 926.
  • Segers, J. (2018) “Comments on « Human life is unlimited – but short » by H. Rootzén and D. Zholud”, Extremes 21(3), 387–390.
  • Segers, J. (2012) “Nonparametric inference for max-stable dependence. Discussion of «Statistical modelling of spatial extremes» by Anthony C. Davison, Simone Padoan and Mathieu Ribatet”, Statistical Science 27(2), 193–196.
  • Segers, J. (2011) “Diagonal sections of bivariate Archimedean copulas. Discussion of «Inference in multivariate Archimedean copula models» by Christian Genest, Johanna Neslehova, and Johanna Ziegel”, TEST  20, 281–283.
  • Segers, J. (2006) “Discussion of «Copulas: Tales and Facts» by Thomas Mikosch”, Extremes  9, 51–53.
  • Beirlant, J., Joossens, E. and Segers, J. (2004) “Discussion of «Generalized Pareto Fit to the Society of Actuaries' Large Claims Database» by A. Cebrián, M. Denuit and Ph. Lambert”, North American Actuarial Journal  8, 108–111.
  • Segers, J. (2004) “Discussion of «A conditional approach for multivariate extreme values» by J.E. Heffernan and J.A. Tawn”, Journal of the Royal Statistical Society, Series B (Statistical Methodology)  66, 542–543.

Book reviews

  • Segers, J. (2009) “Review of: Markovich, N. (2009) Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice”, Journal of the American Statistical Association  104, 863.
  • Segers, J. (2001) “A subtle art. Review of: Coles, S. (2001) An Introduction to statistical modeling of extreme values”, Extremes  4, 379–381.

Science vulgarisation

  • Beirlant, J., Schoutens, W. and Segers, J. (March 2005) “Mandelbrot's Extremism”, Wilmott Magazine, 97–103.
  • Einmahl, J.H.J. and Segers, J. (November 2005) “Van observatie tot extrapolatie: gefundeerde methoden voor de analyse van extreme gebeurtenissen”, De Actuaris, 39–41.
  • Segers, J. (2005) “Extreme-value copulas”, Medium Econometrische Toepassingen 13(1), 9–11.
  • Segers, J. (2004) “Modelling Large Insurance Claims”, Defacto 18(2), 37–40.
  • Segers, J. (2004) “Extreme-value copulas”, Nekst 13(1), 38–41.