Yousri Slaoui
- Statistics and Probability top 2%
- Statistical Methods and Inference 35
- Statistical Distribution Estimation and Applications 6
- Statistical Methods and Bayesian Inference 6
- Advanced Statistical Methods and Models 5
- Nephrology top 10%
- Artificial Intelligence top 10%
- Bayesian Methods and Mixture Models 19
- Gaussian Processes and Bayesian Inference 7
- Neural Networks and Applications 7
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- Control Systems and Identification 10
- Co-authors
- Abdelkader MokkademMariane PelletierSalim BouzebdaJean‐Michel HalimiStéphanie RagotPhilippe ZaouiRonan RousselPierre‐Jean Saulnier
- Journals
- SHILAP Revista de lepidopterología (1 paper)PLoS ONE (1 paper)Diabetes Care (1 paper)
In The Last Decade
Yousri Slaoui
50 papers receiving 450 citations
Peers
Comparison fields: 5 of 82
- Statistics and Probability 232
- Nephrology 54
- Artificial Intelligence 186
- Statistics, Probability and Uncertainty 22
- Endocrinology, Diabetes and Metabolism 49
Countries citing papers authored by Yousri Slaoui
This map shows the geographic impact of Yousri Slaoui'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 Yousri Slaoui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yousri Slaoui more than expected).
Fields of papers citing papers by Yousri Slaoui
This network shows the impact of papers produced by Yousri Slaoui. 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 Yousri Slaoui. The network helps show where Yousri Slaoui may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yousri Slaoui, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 4 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 1 | |
| 8 | 2022 | 2 | |
| 9 | 2021 | 14 | |
| 10 | Multivariate distribution function estimation using stochastic approximation method | 2021 | 0 |
| 11 | 2021 | 1 | |
| 12 | 2020 | 1 | |
| 13 | 2020 | 9 | |
| 14 | 2020 | 3 | |
| 15 | 2019 | 0 | |
| 16 | 2019 | 1 | |
| 17 | 2019 | 18 | |
| 18 | Moderate deviation principles for recursive regression estimators defined by stochastic approximation method | 2015 | 5 |
| 19 | 2014 | 6 | |
| 20 | 2014 | 31 |
About Yousri Slaoui
Yousri Slaoui is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty, having authored 61 papers that have together received 458 indexed citations. Recurring topics across this work include Statistical Methods and Inference (35 papers), Bayesian Methods and Mixture Models (19 papers), Control Systems and Identification (10 papers), Gaussian Processes and Bayesian Inference (7 papers), Neural Networks and Applications (7 papers), Statistical Distribution Estimation and Applications (6 papers), Statistical Methods and Bayesian Inference (6 papers) and Advanced Statistical Methods and Models (5 papers). The work is most often cited by research in Statistics and Probability (232 citations), Nephrology (54 citations) and Artificial Intelligence (186 citations). Yousri Slaoui has collaborated with scholars based in France, Tunisia and Lebanon. Frequent co-authors include Abdelkader Mokkadem, Mariane Pelletier, Salim Bouzebda, Jean‐Michel Halimi, Stéphanie Ragot, Philippe Zaoui, Ronan Roussel, Pierre‐Jean Saulnier, Vincent Rigalleau and Elise Gand. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Diabetes Care.
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.