1 citations · 1 across the 6 of their papers we have counts for
7 papers
Multiple testing with the horseshoe
Sayantan Banerjee, Ismaël Castillo, Fanny Villers
We study multiple testing under continuous global--local shrinkage priors, with a focus on the horseshoe prior in high-dimensional sparse settings. While such priors provide adapti…
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…
A Leakage Bound for Confidence Sets after Black-Box Selection
Sayantan Banerjee
In many analyses the object reported at the end is not fixed in advance, but is chosen after a preliminary search over variables, subgroups, transformations, models or contrasts. C…
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…
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…
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…
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…