Ivan Kojadinovic

3.1k total citations
52 papers, 1.8k citations indexed

About

Ivan Kojadinovic is a scholar working on Statistics and Probability, Finance and Management Science and Operations Research. According to data from OpenAlex, Ivan Kojadinovic has authored 52 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Statistics and Probability, 22 papers in Finance and 22 papers in Management Science and Operations Research. Recurrent topics in Ivan Kojadinovic's work include Financial Risk and Volatility Modeling (22 papers), Multi-Criteria Decision Making (17 papers) and Statistical Methods and Inference (14 papers). Ivan Kojadinovic is often cited by papers focused on Financial Risk and Volatility Modeling (22 papers), Multi-Criteria Decision Making (17 papers) and Statistical Methods and Inference (14 papers). Ivan Kojadinovic collaborates with scholars based in France, United States and New Zealand. Ivan Kojadinovic's co-authors include Jun Yan, Michel Grabisch, Patrick Meyer, Mark Holmes, Jean‐Luc Marichal, Katsushige Fujimoto, Marius Hofert, Axel Bücher, Johan Segers and Jean‐François Quessy and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and Information Sciences.

In The Last Decade

Ivan Kojadinovic

51 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ivan Kojadinovic France 22 628 578 458 360 312 52 1.8k
Stefan Sperlich Switzerland 21 873 1.4× 272 0.5× 212 0.5× 384 1.1× 135 0.4× 101 2.2k
Wenceslao González–Manteiga Spain 31 1.8k 2.9× 314 0.5× 264 0.6× 619 1.7× 246 0.8× 172 3.1k
Han Lin Shang Australia 21 498 0.8× 533 0.9× 254 0.6× 300 0.8× 107 0.3× 120 2.0k
Michael Frank Germany 14 345 0.5× 424 0.7× 262 0.6× 220 0.6× 108 0.3× 49 1.4k
Kjersti Aas Norway 18 493 0.8× 277 0.5× 1.3k 2.8× 433 1.2× 435 1.4× 40 2.8k
Alfred Müller Germany 26 960 1.5× 1.5k 2.7× 941 2.1× 307 0.9× 166 0.5× 69 3.2k
Piet de Jong Australia 22 489 0.8× 614 1.1× 499 1.1× 399 1.1× 82 0.3× 79 2.3k
Shuangzhe Liu Australia 22 578 0.9× 214 0.4× 291 0.6× 194 0.5× 95 0.3× 144 1.9k
Georg Ch. Pflug Austria 29 519 0.8× 1.9k 3.3× 966 2.1× 289 0.8× 736 2.4× 171 4.3k
James V. Zidek Canada 30 1.2k 1.9× 493 0.9× 229 0.5× 846 2.4× 300 1.0× 120 3.7k

Countries citing papers authored by Ivan Kojadinovic

Since Specialization
Citations

This map shows the geographic impact of Ivan Kojadinovic'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 Ivan Kojadinovic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Kojadinovic more than expected).

Fields of papers citing papers by Ivan Kojadinovic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivan Kojadinovic. 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 Ivan Kojadinovic. The network helps show where Ivan Kojadinovic may publish in the future.

Co-authorship network of co-authors of Ivan Kojadinovic

This figure shows the co-authorship network connecting the top 25 collaborators of Ivan Kojadinovic. A scholar is included among the top collaborators of Ivan Kojadinovic 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 Ivan Kojadinovic. Ivan Kojadinovic 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.
Kojadinovic, Ivan, et al.. (2024). Copula-like inference for discrete bivariate distributions with rectangular supports. Electronic Journal of Statistics. 18(1).
2.
Kojadinovic, Ivan, et al.. (2023). A class of smooth, possibly data-adaptive nonparametric copula estimators containing the empirical beta copula. Journal of Multivariate Analysis. 201. 105269–105269. 1 indexed citations
3.
Kojadinovic, Ivan, et al.. (2020). Nonparametric sequential change-point detection for multivariate time\n series based on empirical distribution functions. arXiv (Cornell University). 7 indexed citations
4.
Holmes, Mark & Ivan Kojadinovic. (2020). Open-end nonparametric sequential change-point detection based on the\n retrospective CUSUM statistic. arXiv (Cornell University). 2 indexed citations
5.
Bücher, Axel, et al.. (2014). Detecting changes in cross-sectional dependence in multivariate time series. Journal of Multivariate Analysis. 132. 111–128. 35 indexed citations
6.
Bordes, Laurent, et al.. (2012). Semiparametric estimation of a mixture of two linear regressions where one component is known. arXiv (Cornell University). 1 indexed citations
7.
Holmes, Mark, Ivan Kojadinovic, & Jean‐François Quessy. (2012). Nonparametric tests for change-point detection à la Gombay and Horváth. Journal of Multivariate Analysis. 115. 16–32. 42 indexed citations
8.
Kojadinovic, Ivan & Jun Yan. (2012). A Non‐parametric Test of Exchangeability for Extreme‐Value and Left‐Tail Decreasing Bivariate Copulas. Scandinavian Journal of Statistics. 39(3). 480–496. 16 indexed citations
9.
Kojadinovic, Ivan & Jun Yan. (2012). Goodness‐of‐fit testing based on a weighted bootstrap: A fast large‐sample alternative to the parametric bootstrap. Canadian Journal of Statistics. 40(3). 480–500. 27 indexed citations
10.
Kojadinovic, Ivan & Jun Yan. (2011). A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems. SPIRE - Sciences Po Institutional REpository. 2 indexed citations
11.
Kojadinovic, Ivan, Johan Segers, & Jun Yan. (2011). Large-sample tests of extreme-value dependence for multivariate copulas. Canadian Journal of Statistics. 39(4). 703–720. 30 indexed citations
12.
Kojadinovic, Ivan & Jun Yan. (2010). Modeling Multivariate Distributions with Continuous Margins Using the copula R Package. SHILAP Revista de lepidopterología. 21 indexed citations
13.
Kojadinovic, Ivan & Jun Yan. (2010). Modeling Multivariate Distributions with Continuous Margins Using the copula R Package. Journal of Statistical Software. 34(9). 285 indexed citations
14.
Kojadinovic, Ivan & Jun Yan. (2010). Nonparametric rank-based tests of bivariate extreme-value dependence. Journal of Multivariate Analysis. 101(9). 2234–2249. 24 indexed citations
15.
Kojadinovic, Ivan & Christophe Labreuche. (2009). Partially Bipolar Choquet Integrals. IEEE Transactions on Fuzzy Systems. 17(4). 839–850. 6 indexed citations
16.
Kojadinovic, Ivan & Jean‐Luc Marichal. (2008). On the moments and distribution of discrete Choquet integrals from continuous distributions. Journal of Computational and Applied Mathematics. 230(1). 83–94. 2 indexed citations
17.
Grabisch, Michel, Ivan Kojadinovic, & Patrick Meyer. (2008). A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package. RePEc: Research Papers in Economics. 11 indexed citations
18.
Kojadinovic, Ivan & Mark Holmes. (2008). Tests of independence among continuous random vectors based on Cramér–von Mises functionals of the empirical copula process. Journal of Multivariate Analysis. 100(6). 1137–1154. 43 indexed citations
19.
Marichal, Jean‐Luc, Ivan Kojadinovic, & Katsushige Fujimoto. (2006). Axiomatic characterizations of generalized values. Discrete Applied Mathematics. 155(1). 26–43. 15 indexed citations
20.
Kojadinovic, Ivan. (2005). A maximum quadratic entropy principle for capacity identi cation.. European Society for Fuzzy Logic and Technology Conference. 216–221. 1 indexed citations

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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