Automated Author ProfileSheng, Jiliang
Sheng, Jiliang
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.1 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
PRISMA checklist.Supplementary table.
Authors
- Zhou, Xueyin ;
- Cao, Jiasheng ;
- Topatana, Win ;
- Xie, Tianao ;
- Chen, Tianen ;
- Hu, Jiahao ;
- Li, Shijie ;
- Juengpanich, Sarun ;
- Lu, Ziyi ;
- Zhang, Bin ;
- Wang, Kaitai ;
- Feng, Xu ;
- Sheng, Jiliang ;
- Chen, Mingyu
PRISMA checklist.Supplementary table.
Authors
- Zhou, Xueyin ;
- Cao, Jiasheng ;
- Topatana, Win ;
- Xie, Tianao ;
- Chen, Tianen ;
- Hu, Jiahao ;
- Li, Shijie ;
- Juengpanich, Sarun ;
- Lu, Ziyi ;
- Zhang, Bin ;
- Wang, Kaitai ;
- Feng, Xu ;
- Sheng, Jiliang ;
- Chen, Mingyu
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is established when the observations are independent. To effectively construct an interval without complicated asymptotic variance estimation, a jackknife empirical likelihood inference procedure based on the smoothed nonparametric estimation is provided with a Wilks type of result in case of independent observations. When data follow from AR-GARCH models, the relative risk measure with respect to the errors becomes useful and so we propose a corresponding nonparametric estimator. A simulation study and real-life data analysis show that the proposed relative risk measure is useful in monitoring systemic risk.
Authors
- He, Yi ;
- Hou, Yanxi ;
- Peng, Liang ;
- Sheng, Jiliang
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is established when the observations are independent. To effectively construct an interval without complicated asymptotic variance estimation, a jackknife empirical likelihood inference procedure based on the smoothed nonparametric estimation is provided with a Wilks type of result in case of independent observations. When data follow from AR-GARCH models, the relative risk measure with respect to the errors becomes useful and so we propose a corresponding nonparametric estimator. A simulation study and real-life data analysis show that the proposed relative risk measure is useful in monitoring systemic risk.
Authors
- He, Yi ;
- Hou, Yanxi ;
- Peng, Liang ;
- Sheng, Jiliang