Mohamed Chaouch
- Statistics and Probability top 5%
- Artificial Intelligence
- Finance
- Management Science and Operations Research
- Statistics, Probability and Uncertainty
- Co-authors
- Hervé CardotAli GannounJérôme SaraccoChristian de PerettiAbdelwahed TrabelsiJaonary RabarisoaThierry ChâteauCéline Teulière
- Topics
- Statistical Methods and Inference (7 papers)Advanced Statistical Methods and Models (6 papers)Bayesian Methods and Mixture Models (4 papers)
- Journals
- Annals of Operations ResearchComputational Statistics & Data AnalysisJournal of Multivariate Analysis
- Partner nations
- FranceUnited Arab EmiratesTunisia
In The Last Decade
Mohamed Chaouch
11 papers receiving 92 citations
Peers
Comparison fields: 5 of 35
- Statistics and Probability 64
- Artificial Intelligence 32
- Finance 18
- Management Science and Operations Research 16
- Statistics, Probability and Uncertainty 14
Countries citing papers authored by Mohamed Chaouch
This map shows the geographic impact of Mohamed Chaouch'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 Mohamed Chaouch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohamed Chaouch more than expected).
Fields of papers citing papers by Mohamed Chaouch
This network shows the impact of papers produced by Mohamed Chaouch. 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 Mohamed Chaouch. The network helps show where Mohamed Chaouch may publish in the future.
Co-authorship network of co-authors of Mohamed Chaouch
This figure shows the co-authorship network connecting the top 25 collaborators of Mohamed Chaouch. A scholar is included among the top collaborators of Mohamed Chaouch 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 Mohamed Chaouch. Mohamed Chaouch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 11 | |
| 5 | 5 | |
| 6 | 9 | |
| 7 | 1 | |
| 8 | 24 | |
| 9 | 10 | |
| 10 | Estimation de quantiles géométriques conditionnels et non conditionnels | 5 |
| 11 | 22 |
About Mohamed Chaouch
Mohamed Chaouch is a scholar working on Statistics and Probability, Finance and Statistics, Probability and Uncertainty, having authored 11 papers that have together received 95 indexed citations. Recurring topics across this work include Statistical Methods and Inference (7 papers), Advanced Statistical Methods and Models (6 papers) and Bayesian Methods and Mixture Models (4 papers). The work is most often cited by research in Statistics and Probability (64 citations), Statistics, Probability and Uncertainty (14 citations) and Finance (18 citations). Mohamed Chaouch has collaborated with scholars based in France, United Arab Emirates and Tunisia. Frequent co-authors include Hervé Cardot, Ali Gannoun, Jérôme Saracco, Christian de Peretti, Abdelwahed Trabelsi, Jaonary Rabarisoa, Thierry Château, Céline Teulière and Florian Chabot. Their work appears in journals such as Annals of Operations Research, Computational Statistics & Data Analysis and Journal of Multivariate Analysis.
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.