J. Segers

发表

Non‐parametric Bayesian inference on bivariate extremes

Simon Guillotte, F. Perron, J. Segers, 2009, 0911.3270.

Tails of random sums of a heavy-tailed number of light-tailed terms

J. Segers, C. Robert, 2007, math/0703022.

Statistics of Extremes

J. Teugels, J. Beirlant, J. Segers, 2004 .

Tails of correlation mixtures of elliptical copulas

J. Segers, H. Manner, 2009, 0912.3516.

RANK-BASED INFERENCE FOR BIVARIATE EXTREME-VALUE COPULAS

J. Segers, C. Genest, 2007, 0707.4098.

Asymptotics of empirical copula processes under non-restrictive smoothness assumptions

J. Segers, 2010, 1012.2133.

Generalised regular variation of arbitrary order

J. Segers, E. Omey, 2009, 0901.1468.

Maximum Empirical Likelihood Estimation of the Spectral Measure of an Extreme Value Distribution

J. Segers, J. Einmahl, 2008, 0812.3485.

An M-Estimator for Tail Dependence in Arbitrary Dimensions

J. Segers, J. Einmahl, A. Krajina, 2011 .

MAX-STABLE MODELS FOR MULTIVARIATE EXTREMES

J. Segers, 2012, 1204.0332.

Multivariate peaks over thresholds models

J. Segers, H. Rootzén, Jennifer L. Wadsworth, 2016, 1603.06619.

Extreme-value copulas

Johan Segers, Gordon Gudendorf, J. Segers, 2009, 0911.1015.

Extreme value copula estimation based on block maxima of a multivariate stationary time series

Johan Segers, Axel Bücher, J. Segers, 2013, 1311.3060.

Inference for clusters of extreme values

Johan Segers, Christopher A. T. Ferro, J. Segers, 2003 .

An M‐estimator of spatial tail dependence

Johan Segers, Anna Kiriliouk, J. Segers, 2014, 1403.1975.

NONPARAMETRIC BAYESIAN INFERENCE ON BIVARIATE EXTREMES GUILLOTTE

Johan Segers, François Perron, Simon Guillotte, 2010 .

Discussion on the paper by Heffernan and Tawn

Liang Peng, Kanti V. Mardia, Johan Segers, 2004 .

An M-Estimator for Tail Dependence in Arbitrary Dimensions

Johan Segers, John H. J. Einmahl, J. Segers, 2011, 1112.0905.

Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes

Johan Segers, Marc G. Genton, Raphael Huser, 2017, Journal of Computational and Graphical Statistics.

Semiparametric Gaussian copula models: Geometry and rank-based efficient estimation

J. Segers, B. Werker, R. V. D. Akker, 2013 .

On the asymptotic covariance of the empirical copula process

J. Segers, C. Genest, 2009 .

Polar decomposition of regularly varying time series in star-shaped metric spaces

J. Segers, Yuwei Zhao, Thomas Meinguet, 2016, 1604.00241.

On Some Resampling Procedures with the Empirical Beta Copula

J. Segers, H. Tsukahara, A. Kiriliouk, 2019 .

An estimator of the stable tail dependence function based on the empirical beta copula

J. Segers, L. Tafakori, A. Kiriliouk, 2017, Extremes.

Improving Upon the Marginal Empirical Distribution Function When the Copula is Known

J. Segers, B. Werker, Ramon van den Akker, 2008 .

Semiparametric Gaussian Copula Models: Geometry and Efficient Rank-Based Estimation

J. Segers, B. Werker, Ramon van den Akker, 2013 .

A functional limit theorem for dependent sequences with infinite variance stable limits

J. Segers, Bojan Basrak, Danijel Krizmani'c, 2010, 1001.1345.

Bayesian Clustering and Dimension Reduction in Multivariate Extremes

J. Segers, M. Genton, R. Huser, 2017 .

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

J. Segers, M. de Carvalho, M. Warchoł, 2012, 1204.3524.

WHEN UNIFORM WEAK CONVERGENCE FAILS: EMPIRICAL PROCESSES FOR DEPENDENCE FUNCTIONS AND RESIDUALS VIA EPI- AND HYPOGRAPHS 1

J. Segers, Stanislav Volgushev, Axel Bücher, 2014 .

Graphical and uniform consistency of estimated optimal transport plans

J. Segers, 2022, 2208.02508.

Projection Estimates of Constrained Functional Parameters

A. Guillou, J. Segers, Amélie Fils-Villetard, 2005 .

Statistics of Multivariate Extremes

Johan Segers, Jan Beirlant, Jozef L. Teugels, 2005 .

Extremes of Markov random fields on block graphs: max-stable limits and structured H\"usler-Reiss distributions

J. Segers, Stefka Asenova, 2021, 2112.04847.

Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments

J. Segers, Stefka Asenova, 2022, 2209.14938.

Statistical Inference for H\"usler-Reiss Graphical Models Through Matrix Completions

J. Segers, Sebastian Engelke, Manuel Hentschel, 2022, 2210.14292.

Concentration bounds for the empirical angular measure with statistical learning applications

J. Segers, St'ephan Cl'emenccon, A. Sabourin, 2021, Bernoulli.

Invariance properties of limiting point processes and applications to clusters of extremes

J. Segers, Anja Janssen, 2022, 2207.11204.

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling

J. Segers, Franccois Portier, Rémi Leluc, 2022, NeurIPS.

On the normal uniform local domain of attraction for intermediate order statistics

J. Segers, 2001 .

Polar decomposition of regularly varying time series in star-shaped metric spaces

J. Segers, Yuwei Zhao, Thomas Meinguet, 2017, Extremes.

Statistics for tail processes of Markov chains

J. Segers, H. Drees, M. Warchoł, 2015, Extremes.

Comments on “Human life is unlimited – but short” by H. Rootzén and D. Zholud

J. Segers, 2018, Extremes.

Multivariate peaks over thresholds models

J. Segers, H. Rootzén, Jennifer L. Wadsworth, 2017, Extremes.

Extreme value copula estimation based on block maxima of a multivariate stationary time series

J. Segers, Axel Bücher, 2014, Extremes.

Semiparametric Gaussian Copula Models: Geometry and Efficient Rank-Based Estimation

J. Segers, B. Werker, Ramon Van den Akker, 2013, 1306.6658.

Nonparametric inference for max-stable dependence : Discussion of "Statistical Modelling of Spatial Extremes" by A. C. Davison, S. Padoan and M. Ribatet, to appear in Statistical Science

J. Segers, 2012 .

Tail inference using extreme U-statistics

J. Segers, Chen Zhou, Jochem Oorschot, 2022, Electronic Journal of Statistics.

Discussion of " A conditional approach for multivariate extreme values" by J.E. Heffernan and J.A. Tawn

J. Segers, 2004 .

Identifying groups of variables with the potential of being large simultaneously

J. Segers, Anne Sabourin, Maël Chiapino, 2019, Extremes.

Nonparametric estimation of multivariate extreme-value copulas

Johan Segers, Gordon Gudendorf, J. Segers, 2011, 1107.2410.

Non-Parametric Inference for Bivariate Extreme-Value Copulas

J. Segers, 2004 .

Inference for heavy tailed stationary time series based on sliding blocks

J. Segers, Axel Bucher, 2017, 1706.01968.

On the maximum likelihood estimator for the Generalized Extreme-Value distribution

Johan Segers, Axel Bücher, J. Segers, 2016, 1601.05702.

Modelling multivariate extreme value distributions via Markov trees*

J. Segers, Zuoxiang Peng, Shuang Hu, 2022, Scandinavian Journal of Statistics.

Inference on extremal dependence in the domain of attraction of a structured Hüsler–Reiss distribution motivated by a Markov tree with latent variables

J. Segers, G. Mazo, Stefka Asenova, 2021, Extremes.

One- versus multi-component regular variation and extremes of Markov trees

Johan Segers, J. Segers, 2019, Advances in Applied Probability.

Peaks over thresholds modelling with multivariate generalized Pareto distributions

Johan Segers, Holger Rootzén, Jennifer L. Wadsworth, 2016, 1612.01773.

An estimator of the stable tail dependence function based on the empirical beta copula

J. Segers, L. Tafakori, A. Kiriliouk, 2018, Extremes.

PR ] 2 A pr 2 01 2 MAX-STABLE MODELS FOR MULTIVARIATE EXTREMES

J. Segers, 2018 .

A continuous updating weighted least squares estimator of tail dependence in high dimensions

J. Segers, J. Einmahl, A. Kiriliouk, 2017, Extremes.

PR ] 2 0 O ct 2 01 6 Multivariate peaks over thresholds models

J. Segers, H. Rootzén, J. Wadsworth, 2017 .

A continuous updating weighted least squares estimator of tail dependence in high dimensions

Johan Segers, Anna Kiriliouk, J. Segers, 2016, Extremes.

Nonparametric estimation of extremal dependence

J. Segers, A. Kiriliouk, M. Warchoł, 2014, 1411.0414.

A Method of Moments Estimator of Tail Dependence

J. Segers, J. Einmahl, A. Krajina, 2007, 0710.2039.

4th Conference on Extreme Value Analysis: Probabilistic and Statistical Models and their Applications

L. Haan, J. Segers, C. Anderson, 2005 .

Control variate selection for Monte Carlo integration

Johan Segers, Franccois Portier, R'emi Leluc, 2019, Statistics and Computing.

Risk bounds when learning infinitely many response functions by ordinary linear regression

J. Segers, F. Portier, Vincent Plassier, 2020, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

On the weak convergence of the empirical conditional copula under a simplifying assumption

Johan Segers, François Portier, J. Segers, 2015, J. Multivar. Anal..

Nonparametric estimation of an extreme-value copula in arbitrary dimensions

Johan Segers, Gordon Gudendorf, J. Segers, 2009, J. Multivar. Anal..

A functional limit theorem for partial sums of dependent random variables with infinite variance

J. Segers, Bojan Basrak, Danijel Krizmanić, 2010 .

A functional limit theorem for dependent sequences with infinite variance stable limits

J. Segers, Bojan Basrak, Danijel Krizmani'c, 2010, 1001.1345.

Regularly varying multivariate time series

Johan Segers, Bojan Basrak, J. Segers, 2007, 0707.3989.

Testing the Gumbel hypothesis by Galton's ratio

Johan Segers, J. Segers, J. Teugels, 2000 .

Discussion of “Copulas: Tales and facts”, by Thomas Mikosch

Johan Segers, J. Segers, 2006 .

Maximum likelihood estimation for the Fréchet distribution based on block maxima extracted from a time series

Johan Segers, Axel Bucher, J. Segers, 2015, 1511.07613.

Multivariate regular variation of heavy-tailed Markov Chains

J. Segers, 2007, math/0701411.

Approximate Distributions of Clusters of Extremes

Johan Segers, J. Segers, 2005 .

Functionals of clusters of extremes

J. Segers, 2003, Advances in Applied Probability.

The empirical beta copula

Johan Segers, Masaaki Sibuya, Hideatsu Tsukahara, 2016, J. Multivar. Anal..

Tails of multivariate Archimedean copulas

Arthur Charpentier, Johan Segers, J. Segers, 2009, J. Multivar. Anal..

Second-order refined peaks-over-threshold modelling for heavy-tailed distributions

Johan Segers, Jan Beirlant, Elisabeth Joossens, 2009, 0901.1518.

Assessing Confidence Intervals for the Tail Index by Edgeworth Expansions for the Hill Estimator

J. Segers, E. Haeusler, 2005 .

Edgeworth Expansions for the Distribution Function of the Hill Estimator

J. Segers, E. Haeusler, A. Cuntz, 2003 .

Abelian and Tauberian Theorems on the Bias of the Hill Estimator

J. Segers, 2002 .

SFB 823 Inference for heavy tailed stationary time series based on sliding blocks D iscussion P aper

J. Segers, Axel Bücher, 2017 .

Weak convergence of the weighted empirical beta copula process

Johan Segers, Betina Berghaus, J. Segers, 2017, J. Multivar. Anal..

Gradient importance sampling: An efficient statistical extraction methodology of high-sigma SRAM dynamic characteristics

David Bol, Denis Flandre, Johan Segers, 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Multivariate goodness-of-fit tests based on Wasserstein distance

J. Segers, M. Hallin, Gilles Mordant, 2020, Electronic Journal of Statistics.

Multivariate generalized Pareto distributions: Parametrizations, representations, and properties

Johan Segers, Holger Rootzén, Jennifer L. Wadsworth, 2017, J. Multivar. Anal..

Heat and emergency room admissions in the Netherlands

J. Segers, D. Guha-Sapir, A. Kiriliouk, 2018, BMC Public Health.

Measuring dependence between random vectors via optimal transport

Johan Segers, Gilles Mordant, J. Segers, 2021, J. Multivar. Anal..

Monte Carlo integration with a growing number of control variates

Johan Segers, Franccois Portier, J. Segers, 2018, Journal of Applied Probability.

Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence

J. Segers, Franccois Portier, Rémi Leluc, 2023, ArXiv.

On the asymptotic distribution of the mean absolute deviation about the mean

J. Segers, 2014, 1406.4151.

Empirical Tail Copulas for Functional Data

J. Segers, J. Einmahl, 2020, SSRN Electronic Journal.

Marginal standardization of upper semicontinuous processes. With application to max-stable processes

Johan Segers, Anne Sabourin, J. Segers, 2016, Journal of Applied Probability.

Invariance properties of limiting point processes and applications to clusters of extremes

J. Segers, Anja Janssen, 2022, 2207.11204.

Inference on the tail process with application to financial time series modeling

J. Segers, H. Drees, R. A. Davis, 2016, Journal of Econometrics.

Modeling serial extremal dependence

J. Segers, M. Warchoł, R. Davis, 2016 .

Radial-angular decomposition of regularly varying time series in star-shaped metric spaces

J. Segers, Yuwei Zhao, Thomas Meinguet, 2016 .

Statistics for tail processes of Markov chains

J. Segers, H. Drees, M. Warchoł, 2014, 1405.7721.

Erratum to: Extremal indices, geometric ergodicity of Markov chains, and MCMC

J. Rosenthal, G. Roberts, J. Segers, 2010 .

Regularly varying time series in Banach spaces

J. Segers, Thomas Meinguet, 2010, 1001.3262.

A sliding blocks estimator for the extremal index

J. Segers, C. Robert, C. Ferro, 2008, 0812.4233.

Extremal indices, geometric ergodicity of Markov chains, and MCMC

Johan Segers, Gareth O. Roberts, Jeffrey S. Rosenthal, 2006 .

Rare events, temporal dependence, and the extremal index

J. Segers, 2006, Journal of Applied Probability.

Extreme events : dealing with dependance

J. Segers, 2002 .

Weak convergence of empirical copula processes under nonrestrictive smoothness assumptions

Johan Segers, J. Segers, 2010 .

Markov Tail Chains

Johan Segers, Anja Janßen, J. Segers, 2013, Journal of Applied Probability.

Mandelbrot's Extremism

J. Beirlant, J. Segers, W. Schoutens, 2004 .

Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments

J. Segers, Stefka Asenova, 2022, 2209.14938.

Extremes of Markov random fields on block graphs: Max-stable limits and structured Hüsler–Reiss distributions

J. Segers, Stefka Asenova, 2021, Extremes.

Inference on extremal dependence in a latent Markov tree model attracted to a H\"usler-Reiss distribution

J. Segers, G. Mazo, Stefka Asenova, 2020, 2001.09510.

Risk concentration and diversification: Second-order properties

Johan Segers, Dominik D. Lambrigger, Matthias Degen, 2009, 0910.2367.

Nonparametric estimation of the tree structure of a nested Archimedean copula

Johan Segers, Nathan Uyttendaele, J. Segers, 2013, Comput. Stat. Data Anal..

Generalized Pickands estimators for the extreme value index

Johan Segers, J. Segers, 2005 .

On the longest gap between power-rate arrivals

S. Asmussen, J. Segers, J. Ivanovs, 2017, Bernoulli.

When uniform weak convergence fails: empirical processes for dependence functions via epi- and hypographs

Johan Segers, Stanislav Volgushev, Axel Bucher, 2013, 1305.6408.

Nonparametric estimation of pair-copula constructions with the empirical pair-copula

Johan Segers, Ingrid Hobæk Haff, J. Segers, 2012, Comput. Stat. Data Anal..

Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions

J. Segers, Sebastian Engelke, Manuel Hentschel, 2022, Journal of the American Statistical Association.

“Generalized Pareto Fit to the Society of Actuaries’ Large Claims Database,” Ana C. Cebrián, Michel Denuit, and Philippe Lambert, July 2003

Johan Segers, Jan Beirlant, Elisabeth Joossens, 2004 .

Detecting changes in cross-sectional dependence in multivariate time series

Johan Segers, Tom Rohmer, Ivan Kojadinovic, 2012, J. Multivar. Anal..

Invariance properties of limiting point processes and applications to clusters of extremes

J. Segers, Anja Janssen, 2022, Dependence Modeling.

Maxima and near-maxima of a Gaussian random assignment field

J. Segers, Gilles Mordant, 2021, Statistics & Probability Letters.

Supplement to “Peaks over thresholds modelling with multivariate generalized Pareto distributions”

J. Segers, H. Rootzén, A. Kiriliouk, 2017 .

Stability and tail limits of transport-based quantile contours

J. Segers, C. Valk, 2018, 1811.12061.

Multivariate MA( ∞ ) processes with heavy tails and random coeﬀicients

J. Segers, Shuyan Liu, 2011 .