activity
20062025
most citedVelocity Dispersion Profiles of Seven Dwarf Spheroidal Galaxies

302 citations · 1.2k across the 59 of their papers we have counts for

collaborators

60 papers

stat.ME2025★ 2 cited

Parameter Estimation and Inference in a Continuous Piecewise Linear Regression Model

Georg Hahn, Moulinath Banerjee, Bodhisattva Sen

The estimation of regression parameters in one dimensional broken stick models is a research area of statistics with an extensive literature. We are interested in extending such mo…

math.PR2023

Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability

Alberto González-Sanz, Marc Hallin, Bodhisattva Sen

The contribution of this work is twofold. The first part deals with a Hilbert-space version of McCann's celebrated result on the existence and uniqueness of monotone measure-preser…

stat.ME2023★ 1 cited

Multivariate Symmetry: Distribution-Free Testing via Optimal Transport

Zhen Huang, Bodhisattva Sen

The sign test (Arbuthnott, 1710) and the Wilcoxon signed-rank test (Wilcoxon, 1945) are among the first examples of a nonparametric test. These procedures -- based on signs, (absol…

math.ST2023

Empirical partially Bayes multiple testing and compound decisions

Nikolaos Ignatiadis, Bodhisattva Sen

A common task in high-throughput biology is to screen for associations across thousands of units of interest, e.g., genes or proteins. Often, the data for each unit are modeled as…

math.ST2022★ 1 cited

A Kernel Measure of Dissimilarity between Distributions

Zhen Huang, Bodhisattva Sen

Given distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD) -- a parameter that quantifi…

stat.ME2022★ 3 cited

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models

Yangjing Zhang, Ying Cui, Bodhisattva Sen +1

In this paper we study the computation of the nonparametric maximum likelihood estimator (NPMLE) in multivariate mixture models. Our first approach discretizes this infinite dimens…