Kyungchul Song
- Statistics and Probability top 2%
- Statistical Methods and Inference 24
- Advanced Causal Inference Techniques 11
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- Diabetes Management and Research 8
- Growth Hormone and Insulin-like Growth Factors 8
- Finance top 10%
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- Liver Disease Diagnosis and Treatment 13
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- Obesity, Physical Activity, Diet 10
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- Sexual Differentiation and Disorders 9
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- Diabetes and associated disorders 8
- Co-authors
- Hyun Wook ChaeHo-Seong KimYoon‐Jae WhangJunghwan SuhAhreum KwonOliver LintonHan Saem ChoiHye Sun Lee
- Partner nations
- South KoreaCanadaUnited States
In The Last Decade
Kyungchul Song
103 papers receiving 827 citations
Peers
Comparison fields: 5 of 134
- Statistics and Probability 144
- Endocrinology, Diabetes and Metabolism 206
- General Decision Sciences 13
- General Economics, Econometrics and Finance 50
- Finance 54
Countries citing papers authored by Kyungchul Song
This map shows the geographic impact of Kyungchul Song'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 Kyungchul Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyungchul Song more than expected).
Fields of papers citing papers by Kyungchul Song
This network shows the impact of papers produced by Kyungchul Song. 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 Kyungchul Song. The network helps show where Kyungchul Song may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kyungchul Song, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 0 | |
| 9 | 2023 | 7 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 19 | |
| 12 | 2023 | 0 | |
| 13 | 2023 | 6 | |
| 14 | 2023 | 20 | |
| 15 | 2023 | 3 | |
| 16 | 2022 | 7 | |
| 17 | 2022 | 20 | |
| 18 | 2021 | 1 | |
| 19 | 2021 | 1 | |
| 20 | Allele distribution of FMR1 gene in Korean women. | 2002 | 4 |
About Kyungchul Song
Kyungchul Song is a scholar working on Statistics and Probability, Endocrinology, Diabetes and Metabolism and Management Science and Operations Research, having authored 119 papers that have together received 851 indexed citations. Recurring topics across this work include Statistical Methods and Inference (24 papers), Liver Disease Diagnosis and Treatment (13 papers), Advanced Causal Inference Techniques (11 papers), Obesity, Physical Activity, Diet (10 papers), Sexual Differentiation and Disorders (9 papers), Diabetes Management and Research (8 papers), Diabetes and associated disorders (8 papers) and Growth Hormone and Insulin-like Growth Factors (8 papers). The work is most often cited by research in Statistics and Probability (144 citations), Endocrinology, Diabetes and Metabolism (206 citations) and General Decision Sciences (13 citations). Kyungchul Song has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Hyun Wook Chae, Ho-Seong Kim, Yoon‐Jae Whang, Junghwan Suh, Ahreum Kwon, Oliver Linton, Han Saem Choi, Hye Sun Lee, John F. Rabolt and Youngha Choi. Their work appears in journals such as Journal of Econometrics, Frontiers in Nutrition, Biology, Econometric Theory and Growth Hormone & IGF 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.