On this page I have organized my research into the following three categories: Working papers, Publications and Invited Discussions.
Working papers
- Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sources.
Banerjee, T., Gang, B., & He, J. - Nonparametric Empirical Bayes Prediction in Mixed Models.
Banerjee, T., & Sharma, P. - Risk-Shifting, Regulation, and Government Assistance.
Sharma, P., & Banerjee, T.
2023 NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics. - Nonparametric Empirical Bayes Estimation on Heterogeneous Data.
Banerjee, T., Fu, L. J., James, G. M., Mukherjee, G., & Sun, W. - Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
Luo, J., Banerjee, T., Mukherjee, G., & Sun, W.
Publications (*corresponding author)
- Gang, B., & Banerjee, T*. (2024). Large-Scale Multiple Testing of Composite Null Hypotheses Under Heteroskedasticity.
Biometrika (Accepted) [PDF, Code] - Sharma, P., & Banerjee, T*. (2024). Do financial regulators act in the public's interest? A Bayesian latent class estimation framework for assessing regulatory responses to banking crises.
Journal of the Royal Statistical Society: Series A (Accepted) [PDF, Code] - Banerjee, T., Bhattacharya, B. B., & Mukherjee, G. (2024). Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity.
Journal of Computational and Graphical Statistics, 1-12. [PDF, Code] - Banerjee, T., Liu, P., Mukherjee, G., Dutta, S., & Che, H. (2023). Joint modeling of playing time and purchase propensity in massively multiplayer online role-playing games using crossed random effects.
The Annals of Applied Statistics, 17(3), 2533-2554. [PDF, Code] - Sahu, A., Dutta, A., M Abdelmoniem, A., Banerjee, T., Canini, M., & Kalnis, P. (2021). Rethinking gradient sparsification as total error minimization.
Advances in Neural Information Processing Systems, 34, 8133-8146. - Banerjee T., Mukherjee G. & Paul D. (2021). Improved Shrinkage Prediction under a Spiked Covariance Structure.
Journal of Machine Learning Research, 22 (180),1−40. [Code] - Banerjee T., Liu Q., Mukherjee G., & Sun W. (2021). A general framework for empirical Bayes estimation in discrete linear exponential family.
Journal of Machine Learning Research, 22 (67), 1−46. [Code] - Banerjee T., Bhattacharya B. B., & Mukherjee G. (2020). A nearest-neighbor based nonparametric test for viral remodeling in heterogeneous single-cell proteomic data.
Annals of Applied Statistics, Volume 14, no. 4, Pages 1777-1805. [Code] - Banerjee, T., Mukherjee, G., Dutta, S., & Ghosh, P. (2020). A large-scale constrained joint modeling approach for predicting user activity, engagement, and churn with application to freemium mobile games.
Journal of the American Statistical Association, Volume 115, no. 530, Pages 538-554. [PDF, Code]
Best Paper award: 5th International Conference on Business Analytics and Intelligence, 2017 at Indian Institute of Management, Bangalore. - Banerjee, T., Mukherjee, G., & Sun, W. (2020). Adaptive sparse estimation with side information.
Journal of the American Statistical Association, 115(532), 2053-2067. [PDF, Code]
Distinguished Student Paper Award: 2019 ENAR Spring meeting.
Runner-up: 2017 IISA annual conference student poster competition. - Banerjee, T., Mukherjee, G., & Radchenko, P. (2017). Feature screening in large scale cluster analysis.
Journal of Multivariate Analysis, 161, 191-212. [Code] - Cavrois, M., Banerjee, T., Mukherjee, G., Raman, N., Hussien, R., Rodriguez, B. A., ... & Roan, N. R. (2017). Mass cytometric analysis of HIV entry, replication, and remodeling in tissue CD4+ T cells.
Cell reports, 20(4), 984-998.
Invited Discussions
- Discussion of CARS: covariate assisted ranking and screening for large-scale two-sample inference by Cai, Sun and Wang.
Banerjee T and Mukherjee G. Journal of the Royal Statistical Society, Series B (2019), Volume 81, Pages 223-224.