Yuri Goegebeur

3.7k total citations · 2 hit papers
64 papers, 2.4k citations indexed

About

Yuri Goegebeur is a scholar working on Finance, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, Yuri Goegebeur has authored 64 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Finance, 42 papers in Statistics and Probability and 14 papers in Management Science and Operations Research. Recurrent topics in Yuri Goegebeur's work include Financial Risk and Volatility Modeling (47 papers), Statistical Distribution Estimation and Applications (29 papers) and Statistical Methods and Inference (17 papers). Yuri Goegebeur is often cited by papers focused on Financial Risk and Volatility Modeling (47 papers), Statistical Distribution Estimation and Applications (29 papers) and Statistical Methods and Inference (17 papers). Yuri Goegebeur collaborates with scholars based in Denmark, France and Belgium. Yuri Goegebeur's co-authors include Jan Beirlant, Johan Segers, Jozef L. Teugels, Armelle Guillou, Tertius de Wet, Goedele Dierckx, Gunther Matthys, Andrew Gelman, Francis Tuerlinckx and Iven Van Mechelen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the Royal Statistical Society Series B (Statistical Methodology) and The Annals of Statistics.

In The Last Decade

Yuri Goegebeur

56 papers receiving 2.3k citations

Hit Papers

Statistics of Extremes 2004 2026 2011 2018 2004 2004 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yuri Goegebeur Denmark 18 1.3k 869 731 524 341 64 2.4k
Bruno Rémillard Canada 21 1.5k 1.1× 776 0.9× 729 1.0× 781 1.5× 281 0.8× 96 2.8k
Trevor J. Ringrose United Kingdom 12 1.4k 1.1× 818 0.9× 411 0.6× 799 1.5× 597 1.8× 30 3.0k
Rolf–Dieter Reiss Germany 15 1.1k 0.9× 939 1.1× 448 0.6× 401 0.8× 358 1.0× 57 2.3k
Hedibert F. Lopes United States 22 533 0.4× 661 0.8× 270 0.4× 556 1.1× 231 0.7× 66 2.6k
Jan Beirlant Belgium 31 2.2k 1.7× 1.7k 2.0× 1.1k 1.5× 1.0k 2.0× 776 2.3× 134 4.8k
Jozef L. Teugels Belgium 22 1.4k 1.1× 850 1.0× 502 0.7× 689 1.3× 1.1k 3.2× 54 3.1k
Martin Schlather Germany 25 639 0.5× 429 0.5× 671 0.9× 504 1.0× 148 0.4× 87 2.7k
Michael Sørensen Denmark 30 1.4k 1.1× 775 0.9× 116 0.2× 391 0.7× 189 0.6× 68 2.7k
Dani Gamerman Brazil 23 379 0.3× 1.3k 1.5× 422 0.6× 633 1.2× 392 1.1× 71 3.8k
John E. Angus United States 19 554 0.4× 659 0.8× 228 0.3× 379 0.7× 360 1.1× 82 2.6k

Countries citing papers authored by Yuri Goegebeur

Since Specialization
Citations

This map shows the geographic impact of Yuri Goegebeur's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yuri Goegebeur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuri Goegebeur more than expected).

Fields of papers citing papers by Yuri Goegebeur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuri Goegebeur. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yuri Goegebeur. The network helps show where Yuri Goegebeur may publish in the future.

Co-authorship network of co-authors of Yuri Goegebeur

This figure shows the co-authorship network connecting the top 25 collaborators of Yuri Goegebeur. A scholar is included among the top collaborators of Yuri Goegebeur based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yuri Goegebeur. Yuri Goegebeur is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Goegebeur, Yuri, Armelle Guillou, & Jing Qin. (2024). Dependent conditional tail expectation for extreme levels. Stochastic Processes and their Applications. 171. 104330–104330.
2.
Goegebeur, Yuri, Armelle Guillou, & Jing Qin. (2024). Estimation of the conditional tail moment for Weibull‐type distributions. Scandinavian Journal of Statistics. 51(4). 1782–1815.
3.
Goegebeur, Yuri, Armelle Guillou, & Jing Qin. (2024). Estimation of marginal excess moments for Weibull-type distributions. Extremes.
4.
Goegebeur, Yuri, Armelle Guillou, & Jing Qin. (2022). Robust estimation of the conditional stable tail dependence function. Annals of the Institute of Statistical Mathematics. 75(2). 201–231.
5.
Goegebeur, Yuri, et al.. (2021). Conditional marginal expected shortfall. Extremes. 24(4). 797–847. 2 indexed citations
6.
Goegebeur, Yuri, et al.. (2020). Robust nonparametric estimation of the conditional tail dependence coefficient. Journal of Multivariate Analysis. 178. 104607–104607. 5 indexed citations
7.
Goegebeur, Yuri, Armelle Guillou, & Jing Qin. (2017). On kernel estimation of the second order rate parameter in multivariate extreme value statistics. Statistics & Probability Letters. 128. 35–43.
8.
Goegebeur, Yuri, Armelle Guillou, & Gilles Stupfler. (2015). . Repository@Nottingham (University of Nottingham). 10 indexed citations
9.
Beirlant, Jan, et al.. (2015). Bias-corrected estimation of stable tail dependence function. Journal of Multivariate Analysis. 143. 453–466. 15 indexed citations
10.
Wet, Tertius de, et al.. (2015). Kernel regression with Weibull-type tails. Annals of the Institute of Statistical Mathematics. 68(5). 1135–1162. 6 indexed citations
11.
Goegebeur, Yuri, et al.. (2014). A local moment type estimator for the extreme value index in regression with random covariates. Canadian Journal of Statistics. 42(3). 487–507. 18 indexed citations
12.
Dutang, Christophe, Yuri Goegebeur, & Armelle Guillou. (2014). Robust and bias-corrected estimation of the coefficient of tail dependence. Insurance Mathematics and Economics. 57. 46–57. 10 indexed citations
13.
Ip, Edward H., Geert Molenberghs, Shyh‐Huei Chen, Yuri Goegebeur, & Paul De Boeck. (2013). Functionally Unidimensional Item Response Models for Multivariate Binary Data. Multivariate Behavioral Research. 48(4). 534–562. 11 indexed citations
14.
Goegebeur, Yuri & Armelle Guillou. (2009). Goodness-of-fit testing for Weibull-type behavior. Journal of Statistical Planning and Inference. 140(6). 1417–1436. 17 indexed citations
15.
Goegebeur, Yuri, Jan Beirlant, & Tertius de Wet. (2008). LINKING PARETO-TAIL KERNEL GOODNESS-OF-FIT STATISTICS WITH TAIL INDEX AT OPTIMAL THRESHOLD AND SECOND ORDER ESTIMATION. Digital Access to Libraries. 6(1). 51–69. 33 indexed citations
16.
Goegebeur, Yuri, Paul De Boeck, James A. Wollack, & Allan S. Cohen. (2007). A Speeded Item Response Model with Gradual Process Change. Psychometrika. 73(1). 65–87. 55 indexed citations
17.
Goegebeur, Yuri, Jan Beirlant, & Tertius de Wet. (2006). Goodness-of-fit testing and Pareto-tail estimation. University of Southern Denmark Research Portal (University of Southern Denmark). 1 indexed citations
18.
Beirlant, Jan, Tertius de Wet, & Yuri Goegebeur. (2005). A goodness-of-fit statistic for Pareto-type behaviour. Journal of Computational and Applied Mathematics. 186(1). 99–116. 32 indexed citations
19.
Beirlant, Jan & Yuri Goegebeur. (2004). Discussion of the paper 'A conditional approach for multivariate extreme values' by Heffernan, J.E. & J.A. Tawn. Journal of the Royal Statistical Society Series B (Statistical Methodology). 66(539). 2 indexed citations
20.
Beirlant, Jan, Yuri Goegebeur, Jozef L. Teugels, & Johan Segers. (2004). Statistics of Extremes. Wiley series in probability and statistics. 756 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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