What is he best known for?
The fields of study he is best known for:
- Statistics
- Random variable
- Mathematical analysis
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.
His most cited work include:
- Statistics of Extremes: Theory and Applications (1149 citations)
- Inference for clusters of extreme values (288 citations)
- Asymptotics of empirical copula processes under non-restrictive smoothness assumptions (158 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Applied mathematics (36.58%)
- Estimator (34.63%)
- Tail dependence (21.01%)
What were the highlights of his more recent work (between 2016-2021)?
- Applied mathematics (36.58%)
- Estimator (34.63%)
- Tail dependence (21.01%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- The empirical beta copula (32 citations)
- Multivariate peaks over thresholds models (31 citations)
- Multivariate nonparametric estimation of the Pickands dependence function using Bernstein polynomials (27 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Random variable
- Mathematical analysis
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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