5 papers
On High-Dimensional Change-Point Detection Based on Pairwise Distances
Spandan Ghoshal, Bilol Banerjee, Anil K. Ghosh
In change-point analysis, one aims at finding the locations of abrupt distributional changes (if any) in a sequence of multivariate observations. In this article, we propose some n…
A nonparametric test of spherical symmetry applicable to high dimensional data
Bilol Banerjee, Anil K. Ghosh
We develop a test for spherical symmetry of a multivariate distribution that works well even when the dimension of the data is larger than the sample size . We propose…
Exact distribution-free tests of spherical symmetry applicable to high dimensional data
Bilol Banerjee, Anil K. Ghosh
We develop some graph-based tests for spherical symmetry of a multivariate distribution using a method based on data augmentation. These tests are constructed using a new notion of…
On high-dimensional modifications of the nearest neighbor classifier
Annesha Ghosh, Deep Ghoshal, Bilol Banerjee +1
Nearest neighbor classifier is arguably the most simple and popular nonparametric classifier available in the literature. However, due to the concentration of pairwise distances an…
Classification Using Global and Local Mahalanobis Distances
Annesha Ghosh, Anil K. Ghosh, Rita SahaRay +1
We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic…