Indexing & Abstracting
Rabindra Nath Das
The University of Burdwan, India
Rabindra Nath Das is a Professor in the Department of Statistics, The University of Burdwan, Burdwan, West Bengal, India. He holds Ph. D., in Statistics, from The University of Burdwan, India, and Post-Doc from Seoul National University, Seoul, Korea. He has authored about 112 research articles, and along with a research Monograph entitled- Robust Response Surfaces, Regression, and Positive Data Analyses, published from CRC Press, Taylor & Francis, Chapman & Hall. He wrote research articles on Design of experiments, Regression Analysis, Demography, Quality Engineering, Civil Engineering, Epidemiology, Medical sciences, Environmental, Natural sciences etc. His special area of interest is on Design of experiments, Regression analysis, Quality Engineering and Epidemiology. He has received Gopal Kanji Prize 2009 by The Journal of Applied Statistics and Routledge publications for the best article published in volume 36(7), pp. 755-767 of the journal, entitledâ€“ A measure of robust slope-rotatability for second-order response surface experimental designs. He has received certiï¬cate of appreciation for outstanding research by the Editor-In-Chief, Journal of Thyroid Science, given in the Journal Website (for the paperâ€“ Das, R.N. (2011). The Role of Iodine in the Thyroid Status of Mothers and Their Neonates, Thyroid Science, Vol. 6, No. 2, pp. 1-15). He is acting as Editor, Associate Editor, Executive Editor, Editorial Board Member of more than 100 Journals in Statistics, Physical Sciences, Medical Sciences. Recently he had been acting as a Research Professor in Data Science for Knowledge Creation Research Center, Statistics Department, Seoul National University, South Korea for six months from September 2018 to February 2019.
Research Intrest :
* Theoretical Statistics: 1. Response Surface Design of Experiments (Rotatability & Slope-rotatability); 2. Block Design of Experiments; 3. Regression Analysis; 4. Demography; 5. Quality Engineering; 6. Generalized Linear Models (GLMs), joint GLMs, Hierarchical GLMs (HGLMs), Double HGLMs. *Applied Statistics: 7. Quality & reliability improvement experiments; 8. Civil Engineering; 9. Environmental Science; 10. Genetics; 11. Hydrogeology; 12. Bio-statistics; 13. Medical Sciences; 14. Epidemiology on Thyroid disease Liver disease, Diabetes, Carcinoma, Cardiovascular disease, etc.