Jiancheng Jiang | Econometrics and Statistical Economics | Innovative Research Award

Published on by Statistics

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]

References

  1. Google Scholar. (n.d.). Jiancheng Jiang, Google Scholar profile.
    https://scholar.google.com/citations?user=RUBZnz0AAAAJ&hl=en
  2. World Statistics Awards. (n.d.). World Statistics Awards.
    https://statisticsaward.com/

  3. 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

Gangai Selvi | Econometrics and Statistical Economics | Women in Statistics Award

Published on by Statistics

Dr. Gangai Selvi | Econometrics and Statistical Economics | Women in Statistics Award

Tamil Nadu Agricultural University | India

Dr. R. Gangai Selvi, M.Sc., M.Phil., Ph.D., is a distinguished Professor of Statistics in the Department of Physical Sciences and Information Technology at the Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University (TNAU), Coimbatore, known for her exemplary contributions to teaching, research, and academic development. She holds a Ph.D. in Econometrics from TNAU, an M.Phil. and M.Sc. in Statistics, and a B.Sc. from PSG College of Arts and Science, along with a Diploma in Computer Applications and certifications in programming, reflecting a strong foundation in both statistical theory and computational methods. Dr. Gangai Selvi’s areas of specialization encompass Econometrics, Spatial Econometrics, Applied Statistics, Design of Experiments, Data Analytics, Biostatistics, and Statistical Methods, complemented by her proficiency in statistical software including R, SAS, SPSS, SYSTAT, and STATA. She has developed innovative ICT-based learning tools, most notably the “TNAU Statistics Quiz” Android Mobile App and its corresponding software version, both officially recognized with copyrights for their contribution to student learning and competitive exam preparation, including ICAR and other agricultural examinations. In addition, she has mentored numerous students in technology-enhanced education initiatives, guided mobile app development projects, and created educational YouTube content to support interactive learning. Her teaching portfolio spans undergraduate, postgraduate, and doctoral courses, covering a wide range of topics such as Applied Statistics, Statistical Methods, Design of Experiments, Statistical Inference, Sampling Techniques, Econometrics, Biostatistics, Multivariate Analysis, Regression Analysis, and Statistical Quality Control, reflecting her commitment to cultivating analytical and research skills among students. Her research experience includes serving as Principal Investigator and Co-Principal Investigator on major projects, focusing on areas such as big data analytics, crop yield prediction, agricultural variability, and spatial econometric modeling, with outcomes contributing significantly to the field of agricultural statistics. Dr. Gangai Selvi’s scholarly work, combined with her innovative approach to education and technology integration, establishes her as a respected academic, educator, and thought leader in statistical and econometric research.

Profiles: Google Scholar | Orcid

Featured Publications

Muhammad Mansab | Financial Statistics and Analytics | Young Scientist Award

Published on by Statistics

Mr. Muhammad Mansab | Financial Statistics and Analytics | Young Scientist Award

Liaoning Technical University | China

Mr. Muhammad Mansab is a researcher and engineer whose academic and professional trajectory reflects strong expertise in manufacturing engineering, sustainable supply chain management, and artificial intelligence applications. He holds a Bachelor of Science in Electrical Technology from the University of Lahore and a Master of Science in Project Management from the Superior College Lahore, both with outstanding academic results. His professional background includes extensive industrial experience as a Production Engineer at Escorts (Pvt. Ltd), Lahore, where he led projects involving electrical systems, energy metering, and continuous process optimization under USAID AMI initiatives. Currently affiliated with Liaoning Technical University as a Research Assistant, he contributes to advanced studies in green supply chain management, focusing on financial barrier mitigation, green bonds, and public–private partnership risk reduction. Mansab has authored and co-authored several peer-reviewed documents in areas such as sustainable manufacturing, artificial intelligence in healthcare, social commerce adoption, and human-factor reliability in intelligent construction. His scholarly record includes over 8 citations, an h-index of 1, and 2 indexed documents, reflecting an emerging research profile. Proficient in SPSS, R, Smart PLS (SEM), and Python, he integrates data-driven approaches to enhance firm performance through green innovation mediation. Recognized for academic excellence with the HEC Merit Scholarship and multiple international faculty development programs, he is also known for cross-cultural collaboration between China and South Asia, and is fluent in English and Urdu with basic proficiency in Mandarin.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Sadiq, S., Kaiwei, J., Aman, I., & Mansab, M. (2025). Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI in SC adoption. European Research on Management and Business Economics, 31(1), 100268.

Hassan, S., Irfan, D., Nasim, F., Yakoi, P. S., Mansab, M., & Zubair, S. (2024). Room occupancy detection using IoT sensor data and machine learning. International Journal of Social Science Archives (IJSSA), 7(3).

Lixia, N., Jia’ao, W., Mansab, M., Wen, S., & Fenglian, X. (2025). Study on human factor reliability under the dynamic evolution of intelligent lifting construction accidents. American Journal of Multidisciplinary Research and Innovation.

Tapur, A., Ishtiaque, M., & FengYi, J. (2024). Artificial intelligence in healthcare: Ethical considerations and potential impact in Pakistan. International Journal of Social Science Archives (IJSSA), 7(3).

Lixia, N., & Mansab, M. (2025). Overcoming financial barriers in GSCM: Strategies for sustainable manufacturing in China and Pakistan. Process Integration and Optimization for Sustainability, 1–24.

Xhavit Islami | Econometrics and Statistical Economics | Best Researcher Award

Published on by Statistics

Assist Prof. Dr. Xhavit Islami | Econometrics and Statistical Economics | Best Researcher Award

AAB College | Albania

Assist Prof. Dr. Xhavit Islami is a leading academic in Management and Strategic Management at the Faculty of Economics, AAB College, Republic of Kosovo. He earned his PhD in Organizational Sciences and Management from the University of “Ss. Cyril and Methodius” in North Macedonia and has since built a strong record of research and scholarly contributions. Professor Islami has completed 35 research projects and is currently engaged in five ongoing projects, demonstrating his active involvement in advancing knowledge in management, strategic decision-making, human resource management, and supply chain management. He has published 16 articles in high-impact, Scopus-indexed journals and authored two books, reflecting his commitment to rigorous scholarship. His research work has earned him a Scopus h-index of 4 with 122 citations, highlighting the influence of his publications within the international academic community. He has also participated in five consultancy and industry-sponsored projects, bridging the gap between theory and practice. Professor Islami has collaborated on significant initiatives, including the “EDU-LAB Horizon” Project (2025–2027), and serves on the Scientific Committee of AAB College. His studies focus on innovative strategies, organizational performance, and the integration of artificial intelligence in management practices, providing practical insights for businesses and policymakers. Through his research, he has contributed to the understanding of competitive advantage, sustainable growth, and organizational effectiveness. Professor Islami maintains active professional profiles on Scopus, ORCID, ResearchGate, SSRN, and Academia.edu, ensuring his work is accessible and widely recognized. His scholarly achievements, combined with his ongoing research and industry collaborations, position him as a prominent figure in management and strategic studies, making him a highly deserving candidate for the Best Researcher Award.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“When and How Does Innovation Augment the Effect of HRM on SME Performance?”

“Artificial intelligence and value-based strategy: a literature review and future research directions”

“Lean manufacturing and firms’ financial performance: the role of strategic supplier partnership and information sharing”

“Does competitive strategy moderate the linkage between HRM practices and company performance”

“The Role of Internal Human Resource Orchestration on Firm Performance”