Sunghoon Kwon

988 citations
39 papers · 766 · h-index 15

Impact in

Papers in

Sunghoon Kwon

36 papers receiving 742 citations

Peers

Sunghoon Kwon
Comparison fields: 5 of 137
  • Statistics and Probability 176
  • Biomedical Engineering 199
  • Cellular and Molecular Neuroscience 80
  • Computational Mathematics 2
  • Metals and Alloys 8
Replace Yuchen Zhang with:
Yuchen Zhang China
Dong Xiang China
Jianbo Chen China
Yihang Wang China
Peter Knoll Austria
Yashu Liu China
Sangwoo Park South Korea
Hyunwoo Kim South Korea
Xuemei Dong China
Sunghoon Kwon relative to Yuchen Zhang China Yuchen Zhang's profile →
Citations per field
00.5×10×20×28.5×
Yuchen Zhang · 1×
Citations per year

Countries citing papers authored by Sunghoon Kwon

Since Specialization
Citations

This map shows the geographic impact of Sunghoon Kwon'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 Sunghoon Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunghoon Kwon more than expected).

Fields of papers citing papers by Sunghoon Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunghoon Kwon. 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 Sunghoon Kwon. The network helps show where Sunghoon Kwon may publish in the future.

Co-authors

The 25 scholars most cited alongside Sunghoon Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sunghoon Kwon Line = papers co-authored together Sunghoon Kwon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007142
2 2011118
3 201099
4
Consistent model selection criteria on high dimensions
201258
5 201229
6 201028
7 200628
8 201527
9 201121
10
A New Algorithm and Theory for Penalized Regression-based Clustering.
201620
11 201320
12 200318
13 201617
14 201917
15
LARGE SAMPLE PROPERTIES OF THE SCAD-PENALIZED MAXIMUM LIKELIHOOD ESTIMATION ON HIGH DIMENSIONS
201216
16 201715
17 201312
18 201612
19 201511
20 201710

About Sunghoon Kwon

Sunghoon Kwon is a scholar working on Statistics and Probability, Control and Systems Engineering, Computational Mechanics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 39 papers that have together received 766 indexed citations. Recurring topics across this work include Statistical Methods and Inference (19 papers), Advanced Statistical Methods and Models (7 papers), Control Systems and Identification (7 papers), Sparse and Compressive Sensing Techniques (5 papers), Statistical Methods and Bayesian Inference (4 papers), Face and Expression Recognition (4 papers), Bayesian Methods and Mixture Models (3 papers) and Fault Detection and Control Systems (3 papers). The work is most often cited by research in Statistics and Probability (176 citations), Biomedical Engineering (199 citations), Cellular and Molecular Neuroscience (80 citations), Computational Mathematics (2 citations) and Metals and Alloys (8 citations). Sunghoon Kwon has collaborated with scholars based in South Korea, United States and Puerto Rico. Frequent co-authors include Yongdai Kim, Hosik Choi, Sangin Lee, Seok Hoon Hong, Soojin Shim, Heekyoung Kang, Jeyong Yoon, Joonseon Jeong, Kyung Hyun Ahn and Youdan Kim. Their work appears in journals such as Computational Statistics & Data Analysis, Genetics, Statistica Sinica, Nano Letters and Journal of Real Estate Portfolio Management.

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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