Jinchi Lv
- Statistics and Probability top 0.1%
- Artificial Intelligence top 1%
- Molecular Biology top 10%
- Computational Mechanics top 2%
- Genetics top 5%
- Co-authors
- Jianqing FanYingying FanEmmanuel J. CandèsLucas JansonQi LeiGareth JamesPeter RadchenkoSungshin Kim
- Topics
- Statistical Methods and Inference (26 papers)Statistical Methods and Bayesian Inference (7 papers)Bayesian Methods and Mixture Models (7 papers)
- Journals
- Proceedings of the National Academy of SciencesNucleic Acids ResearchJournal of the American Statistical Association
- Partner nations
- United StatesChinaJapan
In The Last Decade
Jinchi Lv
38 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Statistics and Probability 2.3k
- Artificial Intelligence 1.1k
- Molecular Biology 705
- Computational Mechanics 380
- Genetics 360
Countries citing papers authored by Jinchi Lv
This map shows the geographic impact of Jinchi Lv'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 Jinchi Lv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinchi Lv more than expected).
Fields of papers citing papers by Jinchi Lv
This network shows the impact of papers produced by Jinchi Lv. 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 Jinchi Lv. The network helps show where Jinchi Lv may publish in the future.
Co-authorship network of co-authors of Jinchi Lv
This figure shows the co-authorship network connecting the top 25 collaborators of Jinchi Lv. A scholar is included among the top collaborators of Jinchi Lv 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 Jinchi Lv. Jinchi Lv is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 16 | |
| 4 | 13 | |
| 5 | 13 | |
| 6 | 14 | |
| 7 | 2 | |
| 8 | 16 | |
| 9 | 31 | |
| 10 | Asymptotic Theory of Eigenvectors for Large Random Matrices | 4 |
| 11 | 24 | |
| 12 | Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selectionbreakdown → | 302 |
| 13 | 3 | |
| 14 | 1 | |
| 15 | 122 | |
| 16 | 24 | |
| 17 | 74 | |
| 18 | 233 | |
| 19 | 428 | |
| 20 | 26 |
About Jinchi Lv
Jinchi Lv is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics, having authored 42 papers that have together received 3.9k indexed citations. Recurring topics across this work include Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (7 papers) and Bayesian Methods and Mixture Models (7 papers). The work is most often cited by research in Statistics and Probability (2.3k citations), Artificial Intelligence (1.1k citations) and Finance (327 citations). Jinchi Lv has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Jianqing Fan, Yingying Fan, Emmanuel J. Candès, Lucas Janson, Qi Lei, Gareth James, Peter Radchenko, Sungshin Kim, Nicolas Schweighofer and Kenji Ogawa. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of the American Statistical Association.
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.