Trambak Banerjee

Trambak Banerjee

Assistant Professor - Analytics, Information and Operations at the University of Kansas, School of Business

links

nonacademic

publications

Publications and Preprints

Published in , 2009

softwares

Software

  1. asus - An R package that implements the ASUS (Adpative SURE thresholding with Side Information) procedure for estimating a high-dimensional sparse parameter when along with the primary data we can also gather side information from secondary data sources.
  2. fusionclust - An R package for clustering and feature screening in large scale problems. In particular, fusionclust provides the Big Merge Tracker (BMT) and COSCI algorithms for convex clustering and feature screening using an ?1 fusion penalty.
  3. truh - An R package that implements the TRUH test statistic for nonparametric two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020) for more details.
  4. casp - An R package for Coordinate-wise Adaptive Shrinkage Prediction in a high-dimensional non-exchangeable hierarchical Gaussian model with an unknown location as well as an unknown spiked covariance structure.
  5. cezij - A MATLAB toolbox for simultaneous and hierarchical selection of fixed and random effects in high-dimensional penalized generalized linear mixed models.

teaching

Teaching

, , 1900

University of Kansas

  1. BSAN 450: Data Mining & Predictive Analytics (undergraduate core) - Spring 2021 - 2026

    The primary objective of this course is to enable students to explain and perform statistical analysis of data, with the view of being able to critically evaluate statistical reports or findings. The main focus of the course is Time Series analysis and includes an introduction to ARCH and GARCH models. Since Spring 2024, I have added an introduction to Conformal Inference for classification problems into the curriculum. This course relies heavily on computer programming using R / Python and the emphasis is on applications.

  2. BSAN 730: Advanced Statistical Learning (graduate elective) - Spring 2021 - 2026

    In this course I focus on the statistical analysis of large-scale data. Students learn how some well-known statistical tools can be adapted for the analysis of Big Data and how the limitations of classical tools have engineered the development of modern techniques for data analysis. I cover topics such as split and conquer techniques for variable selection, scalable Bootstrap, Conformal Inference and a gentle introduction to Time Series forecasting using foundation models such as a Chronos. This course relies heavily on computer programming using R / Python and the emphasis is primarily on business applications. Special thanks to the guest speakers (Weinan Wang, Aniruddha Neogi, Joshua Derenski, Bradley Rava, Jacob Dice, Sara Almohtasib) who have given a lecture in this class and have shared their unique experiences in managing and analyzing large data.

  3. BSAN 934: Applied Bayesian Statistics (Ph.D. elective) - Spring 2026

    This course introduces modern Bayesian methods for data analysis, emphasizing practical applications alongside rigorous foundations. I mainly focus on an applied treatment of classical sampling methods, such as Gibbs sampling and Metropolis–Hastings, and interpretaion of MCMC diagnostics. Boradly, the topics include Bayesian regression, hierarchical and multilevel models, model comparison, and an introduction to nonparametric Bayes. Applications are drawn from economics and business, with hands-on implementation in R / Stan. A book that is particularly relevant for this class is A First Course in Bayesian Statistical Methods by Peter Hoff.