Linglong Kong

1.2k citations
68 papers · 623 indexed · h-index 16
Topics
Statistical Methods and Inference (24 papers)Advanced Statistical Methods and Models (9 papers)Sparse and Compressive Sensing Techniques (8 papers)
Partner nations
CanadaChinaUnited States

In The Last Decade

Linglong Kong

60 papers receiving 610 citations

Peers

Linglong Kong
Comparison fields: 5 of 117
  • Statistics and Probability 208
  • Artificial Intelligence 118
  • Radiology, Nuclear Medicine and Imaging 82
  • Molecular Biology 49
  • Health, Toxicology and Mutagenesis 45
Replace Yun Yang with:
Yun Yang United States
Yang Ning United States
Jinzhu Jia China
Alexander Petersen United States
Mauro Gasparini Italy
Jeongyoun Ahn United States
Jochen Einbeck United Kingdom
Pierre Lafaye de Micheaux France
André Gonçalves United States
Huiming Zhang China
Linglong Kong relative to Yun Yang United States Yun Yang's profile →
Citations per field
00.5×7.5×
Yun Yang · 1×
Citations per year

Countries citing papers authored by Linglong Kong

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Linglong Kong

This figure shows the co-authorship network connecting the top 25 collaborators of Linglong Kong. A scholar is included among the top collaborators of Linglong Kong 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 Linglong Kong. Linglong Kong 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
#WorkIndexed citations
1 1
2 0
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5 1
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8 0
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11 1
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13 19
14 20
15 6
16 28
17
cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection
1
18 11
19 4
20 21

About Linglong Kong

Linglong Kong is a scholar working on Computational Mathematics, Statistics and Probability and Virology, having authored 68 papers that have together received 623 indexed citations. Recurring topics across this work include Statistical Methods and Inference (24 papers), Advanced Statistical Methods and Models (9 papers) and Sparse and Compressive Sensing Techniques (8 papers). The work is most often cited by research in Computational Mathematics (20 citations), Statistics and Probability (208 citations) and Virology (21 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, Guido Gerig and Runze Li. Their work appears in journals such as Journal of the American Statistical Association, PLoS ONE and NeuroImage.

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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