Tae Yeon Kwon

613 citations
15 papers · 358 indexed · 1 hit paper · h-index 4
Topics
Insurance, Mortality, Demography, Risk Management (3 papers)Diabetes, Cardiovascular Risks, and Lipoproteins (3 papers)Statistical Methods and Bayesian Inference (2 papers)

In The Last Decade

Tae Yeon Kwon

12 papers receiving 353 citations

Hit Papers

A novel adiposity index as an integrated predictor of car...20182026202020232018100200300

Peers

Tae Yeon Kwon
Comparison fields: 5 of 73
  • Cardiology and Cardiovascular Medicine 127
  • Physiology 97
  • Public Health, Environmental and Occupational Health 95
  • Endocrinology, Diabetes and Metabolism 89
  • Epidemiology 68
Replace Michaela Eickemberg with:
Michaela Eickemberg Brazil
Yu Jin Yang South Korea
Anna Karla Carneiro Roriz Brazil
Soyeun Kim South Korea
Belén Vega‐Piñero Spain
Xiaoguang Wu China
Xiang Gu China
Ana Luz Chiapa United States
Josefina Medina‐Lezama United States
Adeseye A. Akintunde Nigeria
Tae Yeon Kwon relative to Michaela Eickemberg Brazil Michaela Eickemberg's profile →
Citations per field
00.5×8.6×
Michaela Eickemberg · 1×
Citations per year

Countries citing papers authored by Tae Yeon Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Tae Yeon Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tae Yeon Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Tae Yeon Kwon. A scholar is included among the top collaborators of Tae Yeon Kwon 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 Tae Yeon Kwon. Tae Yeon Kwon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
#WorkIndexed citations
1 2
2 1
3 0
4 2
5 1
6
A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortalitybreakdown →
301
7 23
8 0
9 3
10 3
11 13
12 3
13 0
14 3
15 3

About Tae Yeon Kwon

Tae Yeon Kwon is a scholar working on Finance, Demography and Statistics and Probability, having authored 15 papers that have together received 358 indexed citations. Recurring topics across this work include Insurance, Mortality, Demography, Risk Management (3 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (3 papers) and Statistical Methods and Bayesian Inference (2 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (127 citations), Endocrinology, Diabetes and Metabolism (89 citations) and Physiology (97 citations). Tae Yeon Kwon has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Yousung Park, Nam Hoon Kim, Sin Gon Kim, Sungwook Yu, Nan Hee Kim, Sei Hyun Baik, Kyung Mook Choi, Dong Seop Choi, Ji A Seo and Kyeong Jin Kim. Their work appears in journals such as Stroke, Scientific Reports and Finance research letters.

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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