Hyune‐Ju Kim
- Oncology top 5%
- Epidemiology top 10%
- Public Health, Environmental and Occupational Health top 10%
- Surgery
- General Health Professions top 5%
- Co-authors
- Eric J. FeuerMichael P. FayDouglas N. MidthuneBinbing YuMichael J. BarrettHuann‐Sheng ChenBill WheelerDennis W. Buckman
- Topics
- Statistical Methods and Inference (11 papers)Statistical Methods and Bayesian Inference (4 papers)Bayesian Methods and Mixture Models (4 papers)
- Cited by
- OncologyHealthEpidemiology
- Partner nations
- United StatesBelarusSouth Korea
In The Last Decade
Hyune‐Ju Kim
16 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 141
- Oncology 695
- Epidemiology 432
- Public Health, Environmental and Occupational Health 240
- Surgery 226
- General Health Professions 213
Countries citing papers authored by Hyune‐Ju Kim
This map shows the geographic impact of Hyune‐Ju Kim'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 Hyune‐Ju Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyune‐Ju Kim more than expected).
Fields of papers citing papers by Hyune‐Ju Kim
This network shows the impact of papers produced by Hyune‐Ju Kim. 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 Hyune‐Ju Kim. The network helps show where Hyune‐Ju Kim may publish in the future.
Co-authorship network of co-authors of Hyune‐Ju Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Hyune‐Ju Kim. A scholar is included among the top collaborators of Hyune‐Ju Kim 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 Hyune‐Ju Kim. Hyune‐Ju Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 33 | |
| 2 | Twenty years since Joinpoint 1.0: Two major enhancements, their justification, and impactbreakdown → | 101 |
| 3 | 25 | |
| 4 | 82 | |
| 5 | 53 | |
| 6 | 47 | |
| 7 | 14 | |
| 8 | 1 | |
| 9 | 45 | |
| 10 | 263 | |
| 11 | 26 | |
| 12 | Kim H‐J, Fay MP, Feuer EJ, Midthune DN, ‘ Permutation tests for joinpoint regression with applications to cancer rates’. Statistics in Medicine 2000 19:335–351breakdown → | 809 |
| 13 | 278 | |
| 14 | 1 | |
| 15 | 5 | |
| 16 | 1 | |
| 17 | 7 |
About Hyune‐Ju Kim
Hyune‐Ju Kim is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 17 papers that have together received 1.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (11 papers), Statistical Methods and Bayesian Inference (4 papers) and Bayesian Methods and Mixture Models (4 papers). The work is most often cited by research in Oncology (695 citations), Health (131 citations) and Epidemiology (432 citations). Hyune‐Ju Kim has collaborated with scholars based in United States, Belarus and South Korea. Frequent co-authors include Eric J. Feuer, Michael P. Fay, Douglas N. Midthune, Binbing Yu, Michael J. Barrett, Huann‐Sheng Chen, Bill Wheeler, Dennis W. Buckman, Jun Luo and Don Green. Their work appears in journals such as Journal of the American Statistical Association, Cancer and Biometrics.
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.