Jun Fan
Impact in
- Statistics and Probability top 0.5%
- Statistical Methods and Inference
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
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- Atrial Fibrillation Management and Outcomes
- Cardiac Arrhythmias and Treatments
Papers in
-
- Atrial Fibrillation Management and Outcomes 22
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- Neural Networks and Applications 8
- Machine Learning and ELM 5
- Co-authors
- Irène Gijbels (1 shared paper)Hans‐Georg Müller (1 shared paper)Mintu P. Turakhia (24 shared papers)Susan Schmitt (14 shared papers)Chi Harold Liu (6 shared papers)Kin K. Leung (3 shared papers)Claire T. Than (7 shared papers)Ding‐Xuan Zhou (7 shared papers)
- Journals
- Journal of the American College of Cardiology (5 papers)Analysis and Applications (5 papers)American Heart Journal (2 papers)Communications on Pure & Applied Analysis (2 papers)The American Journal of Cardiology (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jun Fan
84 papers receiving 2.1k citations
Jun Fan's Hit Papers
Peers
Comparison fields: 5 of 157
- Statistics and Probability 560
- Cardiology and Cardiovascular Medicine 504
- Internal Medicine 50
- Finance 114
- Computational Mechanics 235
Countries citing papers authored by Jun Fan
This map shows the geographic impact of Jun Fan'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 Jun Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Fan more than expected).
Fields of papers citing papers by Jun Fan
This network shows the impact of papers produced by Jun Fan. 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 Jun Fan. The network helps show where Jun Fan may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Fan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 92 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Local Polynomial Modeling and Its Applications Hit paper breakdown → | 1998 | 750 |
| 2 | 2015 | 143 | |
| 3 | 2017 | 86 | |
| 4 | 2014 | 77 | |
| 5 | 2016 | 72 | |
| 6 | 2014 | 66 | |
| 7 | 2019 | 60 | |
| 8 | 2014 | 59 | |
| 9 | 2019 | 53 | |
| 10 | 2020 | 50 | |
| 11 | A Statistical Learning Approach to Modal Regression | 2020 | 46 |
| 12 | 2014 | 44 | |
| 13 | 2017 | 36 | |
| 14 | 2013 | 35 | |
| 15 | 2014 | 33 | |
| 16 | 2015 | 31 | |
| 17 | 2017 | 24 | |
| 18 | 2019 | 23 | |
| 19 | 2016 | 21 | |
| 20 | 2021 | 20 |
About Jun Fan
Jun Fan is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition and Statistics and Probability, having authored 92 papers that have together received 2.2k indexed citations. Recurring topics across this work include Atrial Fibrillation Management and Outcomes (22 papers), Sparse and Compressive Sensing Techniques (15 papers), Statistical Methods and Inference (8 papers), Neural Networks and Applications (8 papers), Control Systems and Identification (6 papers), Face and Expression Recognition (6 papers), Model Reduction and Neural Networks (5 papers) and Machine Learning and ELM (5 papers). The work is most often cited by research in Statistics and Probability (560 citations), Cardiology and Cardiovascular Medicine (504 citations), Internal Medicine (50 citations), Finance (114 citations) and Computational Mechanics (235 citations). Jun Fan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Irène Gijbels, Hans‐Georg Müller, Mintu P. Turakhia, Susan Schmitt, Chi Harold Liu, Kin K. Leung, Claire T. Than, Ding‐Xuan Zhou, Paul A. Heidenreich and Ting Hu. Their work appears in journals such as Journal of the American College of Cardiology, Analysis and Applications, American Heart Journal, Communications on Pure & Applied Analysis and The American Journal of Cardiology.
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.