Linglong Kong
Impact in
- Computational Mathematics top 5%
- Statistics and Probability top 2%
- Statistical Methods and Inference
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
Papers in
-
- Statistical Methods and Inference 25
- Advanced Statistical Methods and Models 9
- Statistical Methods and Bayesian Inference 6
- Co-authors
- Hongtu Zhu (11 shared papers)Ivan Mizera (4 shared papers)Bei Jiang (18 shared papers)Di Niu (6 shared papers)Weili Lin (4 shared papers)Yijun Zuo (1 shared paper)John H. Gilmore (4 shared papers)Martin Styner (3 shared papers)
- Journals
- Computational Statistics & Data Analysis (4 papers)Journal of Multivariate Analysis (3 papers)PLoS ONE (2 papers)Statistica Sinica (2 papers)Statistical Methods in Medical Research (2 papers)
- Partner nations
- CanadaChinaUnited States
In The Last Decade
Linglong Kong
62 papers receiving 629 citations
Peers
Comparison fields: 5 of 110
- Computational Mathematics 20
- Statistics and Probability 212
- Virology 19
- Artificial Intelligence 125
- Statistics, Probability and Uncertainty 26
Countries citing papers authored by Linglong Kong
This map shows the geographic impact of Linglong Kong'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 Linglong Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linglong Kong more than expected).
Fields of papers citing papers by Linglong Kong
This network shows the impact of papers produced by Linglong Kong. 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 Linglong Kong. The network helps show where Linglong Kong may publish in the future.
Co-authors
The 25 scholars most cited alongside Linglong Kong, 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 71 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 61 | |
| 2 | 2013 | 47 | |
| 3 | 2016 | 47 | |
| 4 | 2020 | 31 | |
| 5 | 2018 | 28 | |
| 6 | 2019 | 25 | |
| 7 | 2019 | 25 | |
| 8 | 2010 | 22 | |
| 9 | 2015 | 21 | |
| 10 | 2019 | 20 | |
| 11 | 2020 | 20 | |
| 12 | 2019 | 19 | |
| 13 | 2019 | 19 | |
| 14 | 2014 | 17 | |
| 15 | 2022 | 16 | |
| 16 | 2021 | 16 | |
| 17 | 2019 | 16 | |
| 18 | 2019 | 13 | |
| 19 | 2022 | 12 | |
| 20 | 2017 | 11 |
About Linglong Kong
Linglong Kong is a scholar working on Statistics and Probability, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computational Mechanics and Control and Systems Engineering, having authored 71 papers that have together received 642 indexed citations. Recurring topics across this work include Statistical Methods and Inference (25 papers), Advanced Statistical Methods and Models (9 papers), Sparse and Compressive Sensing Techniques (8 papers), Advanced Neuroimaging Techniques and Applications (8 papers), Control Systems and Identification (6 papers), Statistical Methods and Bayesian Inference (6 papers), HIV Research and Treatment (5 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Computational Mathematics (20 citations), Statistics and Probability (212 citations), Virology (19 citations), Artificial Intelligence (125 citations) and Statistics, Probability and Uncertainty (26 citations). Linglong Kong has collaborated with scholars based in Canada, China and United States. Frequent co-authors include Hongtu Zhu, Ivan Mizera, Bei Jiang, Di Niu, Weili Lin, Yijun Zuo, John H. Gilmore, Martin Styner, Runze Li and Guido Gerig. Their work appears in journals such as Computational Statistics & Data Analysis, Journal of Multivariate Analysis, PLoS ONE, Statistica Sinica and Statistical Methods in Medical Research.
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.