activity
20172024
most citedConsistent detection and optimal localization of all detectable change points in piecewise stationary arbitrarily sparse network-sequences

4 citations · 9 across the 10 of their papers we have counts for

collaborators

8 papers

stat.ME2022

Learning with latent group sparsity via heat flow dynamics on networks

Subhroshekhar Ghosh, Soumendu Sundar Mukherjee

Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticia…

math.PR20212 cited

Distribution of Eigenvalues of Matrix Ensembles arising from Wigner and Palindromic Toeplitz Blocks

Keller Blackwell, Neelima Borade, Arup Bose +7

Random Matrix Theory (RMT) has successfully modeled diverse systems, from energy levels of heavy nuclei to zeros of -functions; this correspondence has allowed RMT to successful…

math.ST2020

High dimensional PCA: a new model selection criterion

Abhinav Chakraborty, Soumendu Sundar Mukherjee, Arijit Chakrabarti

Given a random sample from a multivariate population, estimating the number of large eigenvalues of the population covariance matrix is an important problem in Statistics with wide…

stat.ME20204 cited

Consistent detection and optimal localization of all detectable change points in piecewise stationary arbitrarily sparse network-sequences

Sharmodeep Bhattacharyya, Shirshendu Chatterjee, Soumendu Sundar Mukherjee

We consider the offline change point detection and localization problem in the context of piecewise stationary networks, where the observable is a finite sequence of networks. We d…

stat.ME2020

Exact Tests for Offline Changepoint Detection in Multichannel Binary and Count Data with Application to Networks

Shyamal K. De, Soumendu Sundar Mukherjee

We consider offline detection of a single changepoint in binary and count time-series. We compare exact tests based on the cumulative sum (CUSUM) and the likelihood ratio (LR) stat…

stat.ML20192 cited

Graphon Estimation from Partially Observed Network Data

Soumendu Sundar Mukherjee, Sayak Chakrabarti

We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We exten…