Inference on covariance operators via concentration inequalities: K-sample tests, classification, and clustering via rademacher complexities
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Kashlak, AB
Aston, JAD
Nickl, R
Abstract
We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applications including a k sample test for equality of covariance, a functional data classifier, and an expectation-maximization style clustering algorithm are derived and tested on both simulated and phoneme data.
Description
Keywords
Functional data analysis, Manifold data, Non-asymptotic confidence sets, Concentration of measure
Journal Title
Sankhya: The Indian Journal of Statistics
Conference Name
Journal ISSN
0972-7671
0976-8378
0976-8378
Volume Title
81A
Publisher
Springer Science and Business Media LLC
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (EP/K021672/2)
Engineering and Physical Sciences Research Council (EP/L016516/1)
Engineering and Physical Sciences Research Council (EP/L016516/1)