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20222026
most citedA New Spatio-Temporal Model Exploiting Hamiltonian Equations

1 citations · 1 across the 6 of their papers we have counts for

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stat.ME2026

Projection Diagnostics for Directional Asymmetry and Tail-Ratio Departure in Multivariate Data

Sayantan Banerjee, Soudeep Deb

We study projection-based diagnostics for distinguishing directional asymmetry from tail-ratio departure in multivariate data. The procedure reduces the problem to one-dimensional…

stat.ME2023

Nonparametric Bayes multiresolution testing for high-dimensional rare events

Jyotishka Datta, Sayantan Banerjee, David B. Dunson

In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic stud…

stat.ME2023

Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation

Ksheera Sagar, Jyotishka Datta, Sayantan Banerjee +1

Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally…

stat.ME2022★ 1 cited

A New Spatio-Temporal Model Exploiting Hamiltonian Equations

Satyaki Mazumder, Sayantan Banerjee, Sourabh Bhattacharya

The solutions of Hamiltonian equations are known to describe the underlying phase space of a mechanical system. In this article, we propose a novel spatio-temporal model using a st…

stat.ME2022

Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix

Anindya Bhadra, Ksheera Sagar, David Rowe +2

Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tunin…