Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities

Adam B. Kashlak (), John A. D. Aston () and Richard Nickl ()
Additional contact information
Adam B. Kashlak: University of Cambridge
John A. D. Aston: University of Cambridge
Richard Nickl: University of Cambridge

Sankhya A: The Indian Journal of Statistics, 2019, vol. 81, issue 1, No 8, 214-243

Abstract: 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.

Keywords: Functional data analysis; Manifold data; Non-asymptotic confidence sets; Concentration of measure.; Primary 62G05; Secondary 62G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13171-018-0143-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankha:v:81:y:2019:i:1:d:10.1007_s13171-018-0143-9

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13171

DOI: 10.1007/s13171-018-0143-9

Access Statistics for this article

Sankhya A: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya A: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().