Prof Dr. Niansheng Tang | Statistical Methods | Best Researcher Award

Yunan University | China

Publication Profile

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👨‍🏫 Early Academic Pursuits

Prof. Dr. Niansheng Tang is a distinguished scholar affiliated with Yunnan University, China. His academic journey started with a series of significant achievements and certifications, culminating in his specialization in statistical and nonlinear models. His academic foundation includes attending key international training sessions and engaging with vital academic societies that contributed to his development as a respected academic in his field.

💼 Professional Endeavors and Career

Dr. Tang’s career has been characterized by a series of high-level academic positions. From 2000 to 2020, he held various teaching and research roles, including significant appointments at Yunnan University. He has been deeply involved in statistical research, with a focus on nonlinear models, reproductive dispersion, and statistical inference for incomplete data. His roles have also included administrative and leadership positions within academic organizations and societies.

🔬 Research Focus and Contributions

Dr. Tang’s primary research focuses on statistical modeling, particularly in areas like nonlinear regression, incomplete data inference, and applications in social, economic, and biomedical studies. He has been the principal investigator for multiple funded projects from the Natural Science Foundation of China, Yunnan Province, and the National Social Science Foundation of China. His work has contributed to the understanding of complex data analysis and its application in various fields.

🌍 Impact and Influence

Dr. Tang has made significant contributions to the field of statistics, with his work influencing various sectors including social sciences, economics, and biomedical research. He has been involved with several prominent academic journals and has contributed to the dissemination of knowledge in his field. His research has impacted both theoretical and applied aspects of statistical modeling, and his efforts have been acknowledged globally.

📚 Academic Cites and Recognition

Dr. Tang’s research is widely cited, with his work featured in leading international journals. He has received multiple awards for his contributions to statistical theory and its application in diverse fields. His impact in the statistical community is reflected through various accolades, including recognition as a member of several esteemed academic bodies such as the ISI and the IMS.

💻 Technical Skills and Expertise

With expertise in statistical theory and computational methods, Dr. Tang has advanced the understanding of complex data structures through his work on generalized nonlinear models. His technical proficiency extends to research in incomplete data models and the development of inference methods applicable to small sample sizes, particularly in biomedical studies.

🎓 Teaching Experience

Throughout his career, Dr. Tang has played an important role in mentoring and educating the next generation of statisticians. He has taught various courses at Yunnan University and has supervised numerous graduate students. His teaching style combines theoretical knowledge with practical application, making him a highly regarded educator.

🏛️ Legacy and Future Contributions

Dr. Tang’s legacy in the field of statistics is built on his groundbreaking research, dedication to education, and service to academic societies. Moving forward, he aims to continue influencing the development of statistical methodologies and applying them to emerging areas such as big data analysis and artificial intelligence. His future contributions will likely continue to shape both academic and practical advancements in his field.

🔍 Ongoing Research and Projects

Dr. Tang’s recent and ongoing projects focus on the statistical analysis of reproductive dispersion models, social sciences, and biomedical research. His work continues to garner support from national funding bodies and academic institutions, ensuring the continued impact of his research.

Publication Top Notes

Federated Learning based on Kernel Local Differential Privacy and Low Gradient Sampling

• • Authors: Yi Chen, Dan Chen, Niansheng Tang
  • Journal: IEEE Access 📡
  • Year: 2025 🗓️

The impact of green digital economy on carbon emission efficiency of financial industry: evidence from 284 cities in China

• • Authors: Zhichuan Zhu, Yuxin Wan, Niansheng Tang
  • Journal: Environment, Development and Sustainability 🌍💡
  • Year: 2025 🗓️

Tuning-free sparse clustering via alternating hard-thresholding

• • Authors: Wei Dong, Chen Xu, Jinhan Xie, Niansheng Tang
  • Journal: Journal of Multivariate Analysis 📊
  • Year: 2024 🗓️

Variational Bayesian approach for analyzing interval-censored data under the proportional hazards model

• • Authors: Wenting Liu, Huiqiong Li, Niansheng Tang, Jun Lyu
  • Journal: Computational Statistics and Data Analysis 💻📉
  • Year: 2024 🗓️

Semiparametric Bayesian approach to assess non-inferiority with assay sensitivity in a three-arm trial with normally distributed endpoints

• • Authors: Niansheng Tang, Fan Liang, Depeng Jiang
  • Journal: Computational Statistics 💻📊
  • Year: 2024 🗓️