Innovative Research Award
Jiancheng Jiang,
Great Bay University.
| Jiancheng Jiang |
| Affiliation |
Great Bay University |
| Country |
China |
| Citations |
1,798 |
| h-index |
21 |
| i10-index |
26 |
| Subject Area |
Semi-parametric regression |
| Event |
World Statistics Awards |
Jiancheng Jiang is a researcher affiliated with Great Bay University whose stated subject area is semi-parametric regression. Available profile information reports 1,798 citations, an h-index of 21, and an i10-index of 26. These indicators provide a quantitative overview of the research record presented for this recognition article.[1]
Abstract
This article presents an academic recognition profile for Jiancheng Jiang of Great Bay University, China, with emphasis on semi-parametric regression. The supplied research indicators include 1,798 citations, an h-index of 21, and an i10-index of 26. The profile is contextualized for consideration within the World Statistics Awards.[1]
Keywords
- Semi-parametric regression
- Statistical modeling
- Regression methodology
- Statistical research
- Research impact
- Great Bay University
Introduction
Jiancheng Jiang is affiliated with Great Bay University, China, and works in semi-parametric regression, an area combining flexible modeling with statistical structure. His reported citation and h-index indicators provide measurable evidence of scholarly visibility. This profile summarizes the supplied academic information for consideration in the World Statistics Awards context.[1]
Research Profile
Jiancheng Jiang’s stated research specialization is semi-parametric regression, situated within modern statistical methodology. The supplied profile records 1,798 citations, an h-index of 21, and an i10-index of 26. These indicators can assist in describing the scale and visibility of his scholarly output while providing quantitative context for academic recognition and evaluation.[1]
Research Contributions
Research in semi-parametric regression contributes to statistical analysis by allowing structured relationships while retaining flexibility for complex patterns. Jiang’s identified subject area places his work within this methodological domain. The reported scholarly indicators suggest sustained academic engagement and visibility, although specific contributions should be assessed through individual publications, methods, datasets, and documented research findings.[1]
Publications
The supplied information does not include a complete publication list or individual article titles for Jiancheng Jiang. His Google Scholar profile provides an appropriate source for reviewing indexed scholarly works, citation relationships, and related bibliographic information. Specific publication titles and DOI identifiers should be verified directly from authoritative bibliographic records before formal citation or award documentation.[1]
Research Impact
The reported 1,798 citations, h-index of 21, and i10-index of 26 indicate measurable scholarly visibility associated with the supplied profile. Citation indicators can help contextualize research influence, but they should be interpreted alongside publication quality, methodological significance, collaboration, reproducibility, and field-specific citation practices when evaluating broader academic impact.[1]
Award Suitability
Jiancheng Jiang’s specialization in semi-parametric regression and reported scholarly indicators provide a relevant academic basis for consideration under an innovative research recognition category. Final award suitability should depend on the World Statistics Awards’ formal criteria, verified publications, research originality, documented contributions, and supporting evidence submitted through the appropriate nomination process.[2]
Conclusion
Jiancheng Jiang is presented as a Great Bay University researcher working in semi-parametric regression, with 1,798 reported citations, an h-index of 21, and an i10-index of 26. These details establish a concise academic profile for recognition purposes. Further assessment should rely on verified publications, research contributions, and documented evidence of innovation.[1]
External Links
References
- Google Scholar. (n.d.). Jiancheng Jiang, Google Scholar profile.
https://scholar.google.com/citations?user=RUBZnz0AAAAJ&hl=en
- World Statistics Awards. (n.d.). World Statistics Awards.
https://statisticsaward.com/
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Fan, J., & Jiang, J. (2005). Nonparametric inferences for additive models. Journal of the American Statistical Association, 100(471), 890–907
https://doi.org/10.1198/016214504000001439