His scientific interests lie mostly in Extreme value theory, Statistics, Tail dependence, Copula and Applied mathematics. Johan Segers combines subjects such as Stochastic process and Econometrics with his study of Extreme value theory. Johan Segers has included themes like Mathematical statistics, Range, Bayesian probability and Data analysis in his Econometrics study.
His Statistics study focuses mostly on Multivariate statistics and Bivariate analysis. His Tail dependence research is multidisciplinary, incorporating elements of Clayton copula, Partial derivative and Pure mathematics. His Applied mathematics study incorporates themes from Estimator and Weak convergence.
The scientist’s investigation covers issues in Applied mathematics, Estimator, Tail dependence, Extreme value theory and Statistics. The concepts of his Applied mathematics study are interwoven with issues in Distribution, Covariance matrix, Mathematical optimization, Limit and Monte Carlo method. His Estimator research includes themes of Nonparametric statistics, Copula and Bivariate analysis.
The various areas that Johan Segers examines in his Tail dependence study include Generalized extreme value distribution, Statistical physics, M-estimator and Empirical likelihood. He conducted interdisciplinary study in his works that combined Extreme value theory and Maxima. He is involved in the study of Statistics that focuses on Multivariate statistics in particular.
Johan Segers spends much of his time researching Applied mathematics, Estimator, Tail dependence, Asymptotic distribution and Multivariate statistics. His biological study spans a wide range of topics, including Parametric statistics, Ordinary least squares, Distribution, Limit and Monte Carlo method. The Monte Carlo method study combines topics in areas such as Series and Econometrics.
His research integrates issues of Nonparametric statistics, Covariate and Copula in his study of Estimator. Johan Segers has included themes like Copula and Extreme value theory in his Asymptotic distribution study. His Extremal dependence study, which is part of a larger body of work in Multivariate statistics, is frequently linked to Extreme events, bridging the gap between disciplines.
His primary areas of investigation include Applied mathematics, Estimator, Monte Carlo method, Nonparametric statistics and Multivariate statistics. His Applied mathematics study combines topics from a wide range of disciplines, such as Covariate, Conditional probability distribution, Random variable and Ordinary least squares. His Estimator research is multidisciplinary, incorporating elements of Copula, Tail dependence and Variance reduction.
The various areas that he examines in his Multivariate statistics study include Bernstein polynomial, Econometrics, Combinatorics and Generalized Pareto distribution. His research in Econometrics focuses on subjects like Financial risk, which are connected to Extreme value theory. Asymptotic distribution is a subfield of Statistics that Johan Segers explores.
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Statistics of Extremes: Theory and Applications
Jan Beirlant;Yuri Goegebeur;Johan Segers;JozefL Teugels.
Inference for clusters of extreme values
Christopher A. T. Ferro;Johan Segers.
Journal of The Royal Statistical Society Series B-statistical Methodology (2003)
Interobserver agreement of medical research council sum-score and handgrip strength in the intensive care unit
Greet Hermans;Beatrickx Clerckx;Tine Vanhullebusch;Johan Segers.
Muscle & Nerve (2012)
Gordon Gudendorf;Johan Segers.
Workshop on Copula Theory and Its Application (2010)
Asymptotics of empirical copula processes under non-restrictive smoothness assumptions
Regularly varying multivariate time series
Bojan Basrak;Johan Segers.
Stochastic Processes and their Applications (2009)
RANK-BASED INFERENCE FOR BIVARIATE EXTREME-VALUE COPULAS
Christian Genest;Johan Segers.
Annals of Statistics (2009)
Tails of multivariate Archimedean copulas
Arthur Charpentier;Johan Segers.
Journal of Multivariate Analysis (2009)
Assessment of limb muscle strength in critically ill patients: a systematic review.
Goele Vanpee;Greet Hermans;Johan Segers;Rik Gosselink.
Critical Care Medicine (2014)
Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution
John H. J. Einmahl;Johan Segers.
Annals of Statistics (2009)
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