activity
20232026
most citedOptimal Confidence Bands for Shape-restricted Regression in Multidimensions

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing math.STShow all

5 papers · 1 filter

math.ST2026

Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds

Adam Quinn Jaffe, Leonardo V. Santoro, Bodhisattva Sen

We initiate the study of nonparametric empirical Bayes denoising methods in the setting where both the latent variables and their measurements lie on a compact Riemannian manifold,…

math.ST2026

Empirical Bayes Estimation and Inference via Smooth Nonparametric Maximum Likelihood

Taehyun Kim, Bodhisattva Sen

The empirical Bayes -modeling approach based on the nonparametric maximum likelihood estimator (NPMLE) has been central to large-scale estimation and inference in the normal mea…

math.ST2024

Distribution-free Measures of Association based on Optimal Transport

Nabarun Deb, Promit Ghosal, Bodhisattva Sen

In this paper we propose and study a class of nonparametric, yet interpretable measures of association between two random vectors and taking values in an…

math.ST20241 cited

Optimal Confidence Bands for Shape-restricted Regression in Multidimensions

Ashley, Datta, Somabha Mukherjee +1

In this paper, we propose and study construction of confidence bands for shape-constrained regression functions when the predictor is multivariate. In particular, we consider the c…

math.ST2023

A New Perspective On Denoising Based On Optimal Transport

Nicolas Garcia Trillos, Bodhisattva Sen

In the standard formulation of the denoising problem, one is given a probabilistic model relating a latent variable and an observation $Z \…