Jinchi Lv

7.0k total citations · 3 hit papers
42 papers, 3.9k citations indexed

About

Jinchi Lv is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Jinchi Lv has authored 42 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Statistics and Probability, 17 papers in Artificial Intelligence and 6 papers in Molecular Biology. Recurrent topics in Jinchi Lv's work include Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (7 papers) and Bayesian Methods and Mixture Models (7 papers). Jinchi Lv is often cited by papers focused on Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (7 papers) and Bayesian Methods and Mixture Models (7 papers). Jinchi Lv collaborates with scholars based in United States, China and Japan. Jinchi Lv's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of the American Statistical Association.

In The Last Decade

Jinchi Lv

38 papers receiving 3.7k citations

Hit Papers

Sure Independence Screening for Ultrahigh Dimensional Fea... 2008 2026 2014 2020 2008 2010 2018 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jinchi Lv United States 17 2.3k 1.1k 705 380 360 42 3.9k
Hao Helen Zhang United States 32 1.9k 0.8× 983 0.9× 747 1.1× 322 0.8× 302 0.8× 114 4.5k
Sara van de Geer Switzerland 22 2.4k 1.0× 1.3k 1.2× 475 0.7× 873 2.3× 186 0.5× 64 4.7k
Keith Knight Canada 21 2.1k 0.9× 828 0.8× 388 0.6× 562 1.5× 168 0.5× 37 4.2k
Ya’acov Ritov Israel 31 3.4k 1.5× 1.4k 1.3× 233 0.3× 574 1.5× 146 0.4× 118 5.9k
Peter Hall Australia 36 3.1k 1.3× 1.2k 1.1× 291 0.4× 132 0.3× 123 0.3× 108 4.6k
Yichao Wu United States 25 1.3k 0.6× 868 0.8× 336 0.5× 327 0.9× 110 0.3× 124 2.8k
Heng Peng China 18 1.3k 0.6× 478 0.5× 201 0.3× 132 0.3× 146 0.4× 59 2.0k
Chenlei Leng Singapore 22 1.4k 0.6× 498 0.5× 194 0.3× 179 0.5× 99 0.3× 75 2.1k
Jianhua Z. Huang United States 23 949 0.4× 398 0.4× 271 0.4× 208 0.5× 99 0.3× 68 2.1k
Jiahua Chen Canada 34 2.0k 0.8× 1.1k 1.1× 366 0.5× 43 0.1× 135 0.4× 142 3.5k

Countries citing papers authored by Jinchi Lv

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Kelly, Kevin R., et al.. (2025). DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue. PLoS Computational Biology. 21(6). e1013133–e1013133. 1 indexed citations
2.
Fan, Yingying, et al.. (2024). High-Dimensional Knockoffs Inference for Time Series Data. Journal of the American Statistical Association. 120(551). 1763–1774.
3.
Fan, Jianqing, Yingying Fan, Xiao Han, & Jinchi Lv. (2022). Simple: Statistical Inference on Membership Profiles in Large Networks. Journal of the Royal Statistical Society Series B (Statistical Methodology). 84(2). 630–653. 16 indexed citations
4.
Fan, Yingying, et al.. (2022). Asymptotic properties of high-dimensional random forests. The Annals of Statistics. 50(6). 13 indexed citations
5.
Fan, Yingying, et al.. (2021). DeepLINK: Deep learning inference using knockoffs with applications to genomics. Proceedings of the National Academy of Sciences. 118(36). 13 indexed citations
6.
Huang, Ni, et al.. (2021). Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request. Information Systems Research. 32(3). 914–931. 14 indexed citations
7.
Kong, Yinfei, et al.. (2021). High-Dimensional Interaction Detection With False Sign Rate Control. Journal of Business and Economic Statistics. 40(3). 1234–1245. 2 indexed citations
8.
Fan, Jianqing, Yingying Fan, Xiao Han, & Jinchi Lv. (2020). Asymptotic Theory of Eigenvectors for Random Matrices With Diverging Spikes. Journal of the American Statistical Association. 117(538). 996–1009. 16 indexed citations
9.
Fan, Yingying, et al.. (2019). IPAD: Stable Interpretable Forecasting with Knockoffs Inference. Journal of the American Statistical Association. 115(532). 1822–1834. 31 indexed citations
10.
Fan, Jianqing, Yingying Fan, Xiao Han, & Jinchi Lv. (2019). Asymptotic Theory of Eigenvectors for Large Random Matrices. arXiv (Cornell University). 4 indexed citations
11.
Fan, Yingying, et al.. (2019). SOFAR: Large-Scale Association Network Learning. IEEE Transactions on Information Theory. 65(8). 4924–4939. 24 indexed citations
12.
Candès, Emmanuel J., Yingying Fan, Lucas Janson, & Jinchi Lv. (2018). Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection. Journal of the Royal Statistical Society Series B (Statistical Methodology). 80(3). 551–577. 302 indexed citations breakdown →
13.
Ren, Zhao, et al.. (2018). Tuning-Free Heterogeneous Inference in Massive Networks. Journal of the American Statistical Association. 114(528). 1908–1925. 3 indexed citations
14.
Lu, Yang Young, Jinchi Lv, Jed A. Fuhrman, & Fengzhu Sun. (2017). Towards enhanced and interpretable clustering/classification in integrative genomics. Nucleic Acids Research. 45(20). e169–e169. 1 indexed citations
15.
Zhang, Haixiang, Yinan Zheng, Zhou Zhang, et al.. (2016). Estimating and testing high-dimensional mediation effects in epigenetic studies. Bioinformatics. 32(20). 3150–3154. 122 indexed citations
16.
Fan, Yingying & Jinchi Lv. (2016). Innovated scalable efficient estimation in ultra-large Gaussian graphical models. The Annals of Statistics. 44(5). 24 indexed citations
17.
Kim, Sungshin, Kenji Ogawa, Jinchi Lv, Nicolas Schweighofer, & Hiroshi Imamizu. (2015). Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation. PLoS Biology. 13(12). e1002312–e1002312. 74 indexed citations
18.
Fan, Jianqing & Jinchi Lv. (2011). Nonconcave Penalized Likelihood With NP-Dimensionality. IEEE Transactions on Information Theory. 57(8). 5467–5484. 233 indexed citations
19.
Fan, Jianqing, Yingying Fan, & Jinchi Lv. (2008). High dimensional covariance matrix estimation using a factor model. Journal of Econometrics. 147(1). 186–197. 428 indexed citations
20.
Cai, Tommaso & Jinchi Lv. (2007). Discussion: The Dantzig selector: Statistical estimation when p is much larger than n. The Annals of Statistics. 35(6). 26 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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