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…
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…
Asymptotic Bayes Optimality for Sparse Count Data
Sayantan Paul, Arijit Chakrabarti
Consider a situation of analyzing high-dimensional count data containing an excess of near-zero counts with a small number of moderate or large counts. Assuming that the observatio…
Posterior Contraction rate and Asymptotic Bayes Optimality for one-group shrinkage priors in sparse normal means problem
Sayantan Paul, Arijit Chakrabarti
We consider a high-dimensional sparse normal means model where the goal is to estimate the mean vector assuming the proportion of non-zero means is unknown. We model the mean vecto…