Biography
Dr. Sanat K. Sarkar is an internationally recognized researcher who has made fundamental contributions to the development of the field of multiple testing toward its applications in modern scientific investigations, such as in genomics and brain imaging.
His research has been funded by the National Science Foundation and the National Security Agency, and often been cited in peer-reviewed journals. He has delivered invited talks at numerous national and international conferences.
He co-organized a major conference on Multiple Comparisons funded by the NSF-CBMS and severed on the organizing committees of several international conferences on the same topic. He has served on the editorials boards of several respectable journals, like the Annals of Statistics, the American Statistician, and Sankhya.
Dr. Sarkar has been recognized as a fellow by both the Institute of Mathematical Statistics and the American Statistical Association, and as an elected member of the International Statistical Institute. He was awarded the Musser Award for excellence in research by the Fox School and inducted several times to the Dean’s Research Honor Roll.
Research Interests
- Multiple Testing
- Statistical Methodolgies
- High-Dimensional Statistical Inference
- Multivariate Statistics
Courses Taught
Number | Name | Level |
|---|---|---|
STAT 8104 | Mathematics for Statistics | Graduate |
STAT 9001 | Advanced Statistical Inference I | Graduate |
STAT 9002 | Advanced Statistical Inference II | Graduate |
Selected Publications
Recent
Nandi, S. & Sarkar, S.K. (2024). Further results on controlling the false discovery rate under some complex grouping structure of hypotheses. Journal of Statistical Planning and Inference, 229, 106094-106094. Elsevier BV. doi: 10.1016/j.jspi.2023.07.008.
Sarkar, S.K. & Tang, C.Y. (2022). Adjusting the Benjamini–Hochberg method for controlling the false discovery rate in knockoff-assisted variable selection. Biometrika, 109(4), 1149-1155. Oxford University Press (OUP). doi: 10.1093/biomet/asab066.
Sarkar, S.K. & Zhao, Z. (2022). Local false discovery rate based methods for multiple testing of one-way classified hypotheses. Electronic Journal of Statistics, 16(2). Institute of Mathematical Statistics. doi: 10.1214/22-ejs2080.
Nandi, S., Sarkar, S.K., & Chen, X. (2021). Adapting to one- and two-way classified structures of hypotheses while controlling the false discovery rate. Journal of Statistical Planning and Inference, 215, 95-108. Elsevier BV. doi: 10.1016/j.jspi.2021.02.006.
Sarkar, S., Rom, D., & McTague, J. (2021). Incorporating the sample correlation into the testing of two endpoints in clinical trials. Journal of Biopharmaceutical Statistics, 31(4), 391-402. Informa UK Limited. doi: 10.1080/10543406.2021.1895191.
Sarkar, S. & Nandi, S. (2021). On the Development of a Local FDR-Based Approach to Testing Two-Way Classified Hypotheses. Sankhya B, 83. doi: 10.1007/s13571-020-00247-6.
Chen, X., Doerge, R., & Sarkar, S.K. (2020). A weighted FDR procedure under discrete and heterogeneous null distributions. Biom J, 62(6), 1544-1563. Germany. 10.1002/bimj.201900216
Guo, W. & Sarkar, S. (2020). Adaptive controls of FWER and FDR under block dependence. Journal of Statistical Planning and Inference, 208, 13-24. doi: 10.1016/j.jspi.2018.03.008.
Chen, X. & Sarkar, S. (2020). On Benjamini–Hochberg procedure applied to mid p-values. Journal of Statistical Planning and Inference, 205, 34-45. doi: 10.1016/j.jspi.2019.06.001.
Sarkar, S.K., Chen, A., He, L., & Guo, W. (2019). Group sequential BH and its adaptive versions controlling the FDR. Journal of Statistical Planning and Inference, 199, 219-235. Elsevier BV. doi: 10.1016/j.jspi.2018.07.001.