Ming−Yen Cheng
- Statistics and Probability top 0.5%
- Artificial Intelligence top 5%
- Control and Systems Engineering top 10%
- Economics and Econometrics top 10%
- Statistics, Probability and Uncertainty top 5%
- Co-authors
- Jianqing FanJ. S. MarronPeter HallHau‐Tieng WuLiang PengYu‐Chun ChenToshio HondaThéo Gasser
- Topics
- Statistical Methods and Inference (35 papers)Advanced Statistical Methods and Models (17 papers)Bayesian Methods and Mixture Models (11 papers)
- Partner nations
- TaiwanUnited StatesHong Kong
In The Last Decade
Ming−Yen Cheng
49 papers receiving 889 citations
Peers
Comparison fields: 5 of 131
- Statistics and Probability 520
- Artificial Intelligence 215
- Control and Systems Engineering 115
- Economics and Econometrics 112
- Statistics, Probability and Uncertainty 82
Countries citing papers authored by Ming−Yen Cheng
This map shows the geographic impact of Ming−Yen Cheng'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 Ming−Yen Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming−Yen Cheng more than expected).
Fields of papers citing papers by Ming−Yen Cheng
This network shows the impact of papers produced by Ming−Yen Cheng. 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 Ming−Yen Cheng. The network helps show where Ming−Yen Cheng may publish in the future.
Co-authorship network of co-authors of Ming−Yen Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Ming−Yen Cheng. A scholar is included among the top collaborators of Ming−Yen Cheng 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 Ming−Yen Cheng. Ming−Yen Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 19 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 33 | |
| 6 | 1 | |
| 7 | 10 | |
| 8 | 15 | |
| 9 | 27 | |
| 10 | 6 | |
| 11 | 10 | |
| 12 | 0 | |
| 13 | 7 | |
| 14 | 2 | |
| 15 | ERROR-DEPENDENT SMOOTHING RULES IN LOCAL LINEAR REGRESSION | 4 |
| 16 | 39 | |
| 17 | 68 | |
| 18 | 32 | |
| 19 | On boundary effects of smooth curve estimators | 5 |
| 20 | 8 |
About Ming−Yen Cheng
Ming−Yen Cheng is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 52 papers that have together received 951 indexed citations. Recurring topics across this work include Statistical Methods and Inference (35 papers), Advanced Statistical Methods and Models (17 papers) and Bayesian Methods and Mixture Models (11 papers). The work is most often cited by research in Statistics and Probability (520 citations), Statistics, Probability and Uncertainty (82 citations) and Finance (80 citations). Ming−Yen Cheng has collaborated with scholars based in Taiwan, United States and Hong Kong. Frequent co-authors include Jianqing Fan, J. S. Marron, Peter Hall, Hau‐Tieng Wu, Liang Peng, Yu‐Chun Chen, Toshio Honda, Théo Gasser, Jin‐Ting Zhang and Wenyang Zhang. Their work appears in journals such as Journal of the American Statistical Association, Biophysical Journal and Biometrika.
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.