Publications by Soham Sarkar | EPFL Graph Search

Related publications (6)

On some consistent tests of mutual independence among several random vectors of arbitrary dimensions

Testing for mutual independence among several random vectors is a challenging problem, and in recent years, it has gained significant attention in statistics and machine learning literature. Most of the existing tests of independence deal with only two ran ...

SPRINGER2020

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Soham Sarkar

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notion ...

TAYLOR & FRANCIS LTD2021

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Soham Sarkar

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Microtome Publishing2022

On some graph-based two-sample tests for high dimension, low sample size data

Soham Sarkar

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, ...

SPRINGER2020

CovNet: Covariance networks for functional data on multidimensional domains

Victor Panaretos, Soham Sarkar

Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address ...

WILEY2022