Wei-Chien Chang
Impact in
- Statistics and Probability top 5%
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
- Statistical Distribution Estimation and Applications
Papers in
-
- Bayesian Methods and Mixture Models 5
-
- Advanced Statistical Methods and Models 6
- Co-authors
- Ming‐Wei Chang (1 shared paper)Chih‐Kung Lee (1 shared paper)Jia‐Yush Yen (2 shared papers)
- Journals
- Journal of the American Statistical Association (4 papers)Journal of the Royal Statistical Society Series C (Applied Statistics) (2 papers)Biometrics (1 paper)Biometrika (1 paper)IEEE photonics journal (1 paper)
- Partner nations
- United StatesTaiwanCanada
In The Last Decade
Wei-Chien Chang
12 papers receiving 276 citations
Peers
Comparison fields: 5 of 85
- Statistics and Probability 96
- Computational Mathematics 3
- Artificial Intelligence 159
- Analytical Chemistry 31
- Computer Vision and Pattern Recognition 59
Countries citing papers authored by Wei-Chien Chang
This map shows the geographic impact of Wei-Chien Chang'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 Wei-Chien Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei-Chien Chang more than expected).
Fields of papers citing papers by Wei-Chien Chang
This network shows the impact of papers produced by Wei-Chien Chang. 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 Wei-Chien Chang. The network helps show where Wei-Chien Chang may publish in the future.
Co-authors
The 3 scholars most cited alongside Wei-Chien Chang, 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 | 1983 | 209 | |
| 2 | 1972 | 55 | |
| 3 | 1972 | 16 | |
| 4 | 1979 | 10 | |
| 5 | 1972 | 7 | |
| 6 | 1987 | 6 | |
| 7 | 1976 | 5 | |
| 8 | 1991 | 4 | |
| 9 | 2010 | 3 | |
| 10 | 2020 | 2 | |
| 11 | 1991 | 1 | |
| 12 | 2006 | 1 |
About Wei-Chien Chang
Wei-Chien Chang is a scholar working on Artificial Intelligence, Statistics and Probability, Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition and Finance, having authored 12 papers that have together received 319 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (6 papers), Bayesian Methods and Mixture Models (5 papers), Financial Markets and Investment Strategies (2 papers), Face and Expression Recognition (2 papers), Market Dynamics and Volatility (2 papers), ECG Monitoring and Analysis (2 papers), Genetics and Plant Breeding (1 paper) and Capital Investment and Risk Analysis (1 paper). The work is most often cited by research in Statistics and Probability (96 citations), Computational Mathematics (3 citations), Artificial Intelligence (159 citations), Analytical Chemistry (31 citations) and Computer Vision and Pattern Recognition (59 citations). Wei-Chien Chang has collaborated with scholars based in United States, Taiwan and Canada. Frequent co-authors include Ming‐Wei Chang, Chih‐Kung Lee and Jia‐Yush Yen. Their work appears in journals such as Journal of the American Statistical Association, Journal of the Royal Statistical Society Series C (Applied Statistics), Biometrics, Biometrika and IEEE photonics journal.
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.