7 papers
Covariance-Adaptive Residualization and Stagewise Calibration for Dependent Multiple Testing
Prasenjit Ghosh, Arijit Chakrabarti
In this paper, we study simultaneous hypothesis testing for multivariate Gaussian means under arbitrary covariance dependence. Building upon the Maximum Residual Down (MRD) procedu…
Sharp Asymptotic Minimaxity of the Gavrilov-Benjamini-Sarkar Step-Down Testing Procedure in Sparse Gaussian Sequence Models
Prasenjit Ghosh
False discovery rate control does not by itself guarantee optimal recovery of sparse signals: a procedure may make few false discoveries while missing an unnecessarily large fracti…
Asymptotic Bayes Optimality Under Sparsity of the Gavrilov-Benjamini-Sarkar Step-Down Testing Procedure
Prasenjit Ghosh, Arijit Chakrabarti
In this article, we investigate the asymptotic Bayes optimality under sparsity (ABOS) of the Gavrilov-Benjamini-Sarkar (GBS) step-down multiple testing procedure (Gavrilov et al.,…
Bayesian Model Pursuit and Near-Oracle Sparse Signal Discovery Under Dependence
Prasenjit Ghosh, Arijit Chakrabarti
Sparse signal discovery is a fundamental problem in large-scale inference, where the goal is to identify a small number of active signals hidden among a large collection of null ef…
Admissibility of Adaptive Monotone Step-Down Multiple Testing Procedures Under Arbitrary Covariance Dependence
Prasenjit Ghosh, Arijit Chakrabarti
In this paper, we consider the problem of simultaneous testing of multivariate normal means under arbitrary covariance dependence. Specifically, let $\boldsymbol{X}\sim N_n(\boldsy…
Sharp Asymptotic Minimaxity for Multiple Testing Using One-Group Shrinkage Priors
Sayantan Paul, Prasenjit Ghosh, Arijit Chakrabarti
This paper investigates asymptotic minimaxity properties of Bayesian multiple testing rules in the sparse Gaussian sequence model using a broad class of global-local scale mixtures…
Consistent Group selection using Global-local prior in High dimensional setup
Sayantan Paul, Prasenjit Ghosh, Arijit Chakrabarti
We consider the problem of model selection when grouping structure is inherent within the regressors. Using a Bayesian approach, we model the mean vector by a one-group global-loca…