Jiancheng Jiang
- Statistics and Probability top 0.5%
- Artificial Intelligence top 10%
- Finance top 5%
- Economics and Econometrics top 10%
- Management Science and Operations Research top 10%
- Co-authors
- Jianqing FanJelena BradićXinyuan SongHaibo ZhouJianwen CaiY. P. MackYongxiang HuYer Van Hui
- Topics
- Statistical Methods and Inference (26 papers)Advanced Statistical Methods and Models (20 papers)Statistical Methods and Bayesian Inference (8 papers)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Jiancheng Jiang
44 papers receiving 815 citations
Peers
Comparison fields: 5 of 98
- Statistics and Probability 535
- Artificial Intelligence 153
- Finance 140
- Economics and Econometrics 113
- Management Science and Operations Research 70
Countries citing papers authored by Jiancheng Jiang
This map shows the geographic impact of Jiancheng Jiang'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 Jiancheng Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiancheng Jiang more than expected).
Fields of papers citing papers by Jiancheng Jiang
This network shows the impact of papers produced by Jiancheng Jiang. 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 Jiancheng Jiang. The network helps show where Jiancheng Jiang may publish in the future.
Co-authorship network of co-authors of Jiancheng Jiang
This figure shows the co-authorship network connecting the top 25 collaborators of Jiancheng Jiang. A scholar is included among the top collaborators of Jiancheng Jiang 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 Jiancheng Jiang. Jiancheng Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 7 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | 21 | |
| 11 | 8 | |
| 12 | 2 | |
| 13 | 22 | |
| 14 | ORACLE MODEL SELECTION FOR NONLINEAR MODELS BASED ON WEIGHTED COMPOSITE QUANTILE REGRESSION | 62 |
| 15 | 96 | |
| 16 | 1 | |
| 17 | 39 | |
| 18 | 99 | |
| 19 | 0 | |
| 20 | 18 |
About Jiancheng Jiang
Jiancheng Jiang is a scholar working on Statistics and Probability, Finance and General Economics, Econometrics and Finance, having authored 49 papers that have together received 840 indexed citations. Recurring topics across this work include Statistical Methods and Inference (26 papers), Advanced Statistical Methods and Models (20 papers) and Statistical Methods and Bayesian Inference (8 papers). The work is most often cited by research in Statistics and Probability (535 citations), Finance (140 citations) and General Economics, Econometrics and Finance (52 citations). Jiancheng Jiang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Jianqing Fan, Jelena Bradić, Xinyuan Song, Haibo Zhou, Jianwen Cai, Y. P. Mack, Yongxiang Hu, Yer Van Hui, D. Karthik and Chunming Zhang. Their work appears in journals such as Journal of the American Statistical Association, PLoS ONE and Advanced Functional Materials.
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.