Sounak Chakraborty, Ph.D.
Affiliations: | 2005 | University of Florida, Gainesville, Gainesville, FL, United States |
Area:
Statistics, Artificial Intelligence, Molecular Biology, Biostatistics BiologyGoogle:
"Sounak Chakraborty"Parents
Sign in to add mentor| Malay Ghosh | grad student | 2005 | UF Gainesville | |
| (Bayesian machine learning.) | ||||
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Publications
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| Chakraborty S, Lozano AC. (2019) A graph Laplacian prior for Bayesian variable selection and grouping Computational Statistics & Data Analysis. 136: 72-91 |
| Zhang L, Chakraborty S, Cham J, et al. (2017) Abstract 549: Clustering analysis of next-generation sequencing T cell repertoire data in sipuleucel-T treated prostate cancer patients Cancer Research. 77: 549-549 |
| Lee KH, Chakraborty S, Sun J. (2017) Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior Computational Statistics & Data Analysis. 112: 1-13 |
| Xu C, Chakraborty S. (2017) Bayesian kernel machine models for testing genetic pathway effects in prostate cancer prognosis Statistical Analysis and Data Mining. 10: 378-392 |
| Chakraborty S. (2016) Chapter 7 – Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection Handbook of Statistics. 35: 229-251 |
| Lee KH, Chakraborty S, Sun J. (2015) Survival prediction and variable selection with simultaneous shrinkage and grouping priors Statistical Analysis and Data Mining. 8: 114-127 |
| Chakraborty S, Ghosh M, Mallick BK. (2012) Bayesian nonlinear regression for large p small n problems Journal of Multivariate Analysis. 108: 28-40 |
| Chakraborty S. (2012) Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy Computational Statistics and Data Analysis. 56: 2742-2755 |
| Chakraborty S, Ghosh M. (2012) Applications of Bayesian Neural Networks in Prostate Cancer Study Handbook of Statistics. 28: 241-262 |
| Khalilia M, Chakraborty S, Popescu M. (2011) Predicting disease risks from highly imbalanced data using random forest. Bmc Medical Informatics and Decision Making. 11: 51 |