Song Xi Chen
- Statistics and Probability top 0.05%
- Artificial Intelligence top 1%
- Finance top 1%
- Economics and Econometrics top 2%
- Environmental Engineering top 2%
- Topics
- Statistical Methods and Inference (54 papers)Statistical Methods and Bayesian Inference (27 papers)Advanced Statistical Methods and Models (19 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Song Xi Chen
117 papers receiving 4.1k citations
Peers
Comparison fields: 5 of 149
- Statistics and Probability 2.6k
- Artificial Intelligence 1.0k
- Finance 816
- Economics and Econometrics 429
- Environmental Engineering 421
Countries citing papers authored by Song Xi Chen
This map shows the geographic impact of Song Xi 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 Song Xi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Xi Chen more than expected).
Fields of papers citing papers by Song Xi Chen
This network shows the impact of papers produced by Song Xi 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 Song Xi Chen. The network helps show where Song Xi Chen may publish in the future.
Co-authorship network of co-authors of Song Xi Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Song Xi Chen. A scholar is included among the top collaborators of Song Xi 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 Song Xi Chen. Song Xi 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 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 18 | |
| 9 | Partitioning Structure Learning for Segmented Linear Regression Trees | 1 |
| 10 | 29 | |
| 11 | 9 | |
| 12 | 6 | |
| 13 | Nonparametric Estimation of Expected Shortfall | 6 |
| 14 | Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes | 31 |
| 15 | Nonparametric Inference of Value-at-Risk for Dependent Financial Returns | 4 |
| 16 | Effects of bagging and bias correction on estimators defined by estimating equations | 11 |
| 17 | 2 | |
| 18 | 24 | |
| 19 | 15 | |
| 20 | 95 |
About Song Xi Chen
Song Xi Chen is a scholar working on Statistics and Probability, Finance and Statistics, Probability and Uncertainty, having authored 121 papers that have together received 4.3k indexed citations. Recurring topics across this work include Statistical Methods and Inference (54 papers), Statistical Methods and Bayesian Inference (27 papers) and Advanced Statistical Methods and Models (19 papers). The work is most often cited by research in Statistics and Probability (2.6k citations), Finance (816 citations) and Statistics, Probability and Uncertainty (236 citations). Song Xi Chen has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Yingli Qin, Ping-Shou Zhong, Hengjian Cui, Cheng Yong Tang, Peter Hall, Jun Li, Lixin Zhang, Bin Guo, Ingrid Van Keilegom and Hui Huang. Their work appears in journals such as Journal of the American Statistical Association, Environmental Science & Technology and PLoS ONE.
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.