Hua Yun Chen
- Statistics and Probability top 1%
- Pathology and Forensic Medicine top 5%
- Health, Toxicology and Mutagenesis top 10%
- Psychiatry and Mental health top 10%
- Pollution top 10%
- Co-authors
- Roderick J. A. LittleMarcia FinlaysonVirgil MathiowetzPing LuoKathleen MatuskaC. Y. WangLinda ForstLorraine M. Conroy
- Topics
- Statistical Methods and Bayesian Inference (16 papers)Statistical Methods and Inference (16 papers)Bayesian Methods and Mixture Models (8 papers)
- Cited by
- Statistics and ProbabilityRadiological and Ultrasound TechnologyPathology and Forensic Medicine
- Partner nations
- United StatesChinaSouth Africa
In The Last Decade
Hua Yun Chen
38 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 123
- Statistics and Probability 412
- Pathology and Forensic Medicine 243
- Health, Toxicology and Mutagenesis 137
- Psychiatry and Mental health 133
- Pollution 104
Countries citing papers authored by Hua Yun Chen
This map shows the geographic impact of Hua Yun Chen'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 Hua Yun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hua Yun Chen more than expected).
Fields of papers citing papers by Hua Yun Chen
This network shows the impact of papers produced by Hua Yun Chen. 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 Hua Yun Chen. The network helps show where Hua Yun Chen may publish in the future.
Co-authorship network of co-authors of Hua Yun Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Hua Yun Chen. A scholar is included among the top collaborators of Hua Yun Chen 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 Hua Yun Chen. Hua Yun Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 5 | |
| 3 | 40 | |
| 4 | 47 | |
| 5 | 62 | |
| 6 | 7 | |
| 7 | 15 | |
| 8 | 23 | |
| 9 | 1 | |
| 10 | 2 | |
| 11 | 4 | |
| 12 | 10 | |
| 13 | 68 | |
| 14 | 49 | |
| 15 | 21 | |
| 16 | 70 | |
| 17 | 49 | |
| 18 | 8 | |
| 19 | 70 | |
| 20 | 122 |
About Hua Yun Chen
Hua Yun Chen is a scholar working on Statistics and Probability, Virology and Radiological and Ultrasound Technology, having authored 39 papers that have together received 1.2k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (16 papers), Statistical Methods and Inference (16 papers) and Bayesian Methods and Mixture Models (8 papers). The work is most often cited by research in Statistics and Probability (412 citations), Radiological and Ultrasound Technology (75 citations) and Pathology and Forensic Medicine (243 citations). Hua Yun Chen has collaborated with scholars based in United States, China and South Africa. Frequent co-authors include Roderick J. A. Little, Marcia Finlayson, Virgil Mathiowetz, Ping Luo, Kathleen Matuska, C. Y. Wang, Linda Forst, Lorraine M. Conroy, Maria Argos and Mary Turyk. Their work appears in journals such as Journal of the American Statistical Association, NeuroImage 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.