19 citations · 46 across the 17 of their papers we have counts for
9 papers · 1 filter
Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
Jiajun Luo, Trambak Banerjee, Gourab Mukherjee +1
We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonp…
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivari…
Large-Scale Shrinkage Estimation under Markovian Dependence
Bowen Gang, Gourab Mukherjee, Wenguang Sun
We consider the problem of simultaneous estimation of a sequence of dependent parameters that are generated from a hidden Markov model. Based on observing a noise contaminated vect…
A Nearest-Neighbor Based Nonparametric Test for Viral Remodeling in Heterogeneous Single-Cell Proteomic Data
Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
An important problem in contemporary immunology studies based on single-cell protein expression data is to determine whether cellular expressions are remodeled post infection by a…
Nonparametric Empirical Bayes Estimation on Heterogeneous Data
Trambak Banerjee, Luella J. Fu, Gareth M. James +2
The simultaneous estimation of many parameters based on data collected from corresponding studies is a key research problem that has received renewed attention in the high-dimensio…
Adaptive Sparse Estimation with Side Information
Trambak Banerjee, Gourab Mukherjee, Wenguang Sun
The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general fr…
Feature Screening in Large Scale Cluster Analysis
Trambak Banerjee, Gourab Mukherjee, Peter Radchenko
We propose a novel methodology for feature screening in clustering massive datasets, in which both the number of features and the number of observations can potentially be very lar…
Empirical Bayes Estimates for a 2-Way Cross-Classified Additive Model
Lawrence D. Brown, Gourab Mukherjee, Asaf Weinstein
We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects ex…
Convex clustering via fusion penalization
Peter Radchenko, Gourab Mukherjee
We study the large sample behavior of a convex clustering framework, which minimizes the sample within cluster sum of squares under an~ fusion constraint on the cluster cen…