Subhashis Ghoshal
Statistics
- Phone: 919-513-0190
- Email: sghosal@ncsu.edu
- Office: SAS Hall 4276
- Website: http://www4.stat.ncsu.edu/~ghoshal
Subhashis Ghoshal is a Goodnight Distinguished Professor of Statistics and Operations Research at North Carolina State University.
Research Interests
- High and Infinite Dimensional Models
- Bayesian Inference
- Asymptotic Statistics
- Image Processing
- Functional Data Analysis
- Multiple Hypotheses Testing
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Statistics | Indian Statistical Institute, Calcutta, India | 1995 |
| M.S.Stat. | Master of Science in Statistics | Indian Statistical Institute, Calcutta, India | 1990 |
| B.S.Stat. | Bachelor of Science in Statistics | Indian Statistical Institute, Calcutta, India | 1988 |
Publications
- Posterior contraction and testing for multivariate isotonic regression
- Wang, K., & Ghosal, S. (2023), ELECTRONIC JOURNAL OF STATISTICS, 17(1), 798–822. https://doi.org/10.1214/23-EJS2115
- BAYESIAN INFERENCE ON MULTIVARIATE MEDIANS AND QUANTILES
- Bhattacharya, I., & Ghosal, S. (2022), STATISTICA SINICA, 32(1), 517–538. https://doi.org/10.5705/ss.202020.0108
- Discussion of "Confidence Intervals for Nonparametric Empirical Bayes Analysis" by Ignatiadis and Wager
- Ghosal, S. (2022, September 14), JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 117, pp. 1171–1174. https://doi.org/10.1080/01621459.2022.2093726
- Rates and coverage for monotone densities using projection-posterior
- Chakraborty, M., & Ghosal, S. (2022), BERNOULLI, 28(2), 1093–1119. https://doi.org/10.3150/21-BEJ1379
- Two-step Bayesian methods for generalized regression driven by partial differential equations
- Bhaumik, P., Shi, W., & Ghosal, S. (2022), BERNOULLI, 28(3), 1625–1647. https://doi.org/10.3150/21-BEJ1363
- Bayesian estimation of sparse precision matrices in the presence of Gaussian measurement error
- Shi, W., Ghosal, S., & Martin, R. (2021), ELECTRONIC JOURNAL OF STATISTICS, 15(2), 4545–4579. https://doi.org/10.1214/21-EJS1904
- Posterior contraction in sparse generalized linear models
- Jeong, S., & Ghosal, S. (2021), BIOMETRIKA, 108(2), 367–379. https://doi.org/10.1093/biomet/asaa074
- Unified Bayesian theory of sparse linear regression with nuisance parameters
- Jeong, S., & Ghosal, S. (2021), ELECTRONIC JOURNAL OF STATISTICS, 15(1), 3040–3111. https://doi.org/10.1214/21-EJS1855
- Bayesian linear regression for multivariate responses under group sparsity
- Ning, B., Jeong, S., & Ghosal, S. (2020), BERNOULLI, 26(3), 2353–2382. https://doi.org/10.3150/20-BEJ1198
- Posterior contraction and credible sets for filaments of regression functions
- Li, W., & Ghosal, S. (2020), ELECTRONIC JOURNAL OF STATISTICS, 14(1), 1707–1743. https://doi.org/10.1214/20-EJS1705
