Classification Using Censored Functional Data

Aurore Delaigle and Peter Hall

Journal of the American Statistical Association, 2013, vol. 108, issue 504, 1269-1283

Abstract: We consider classification of functional data when the training curves are not observed on the same interval. Different types of classifier are suggested, one of which involves a new curve extension procedure. Our approach enables us to exploit the information contained in the endpoints of these intervals by incorporating it in an explicit but flexible way. We study asymptotic properties of our classifiers, and show that, in a variety of settings, they can even produce asymptotically perfect classification. The performance of our techniques is illustrated in applications to real and simulated data. Supplementary materials for this article are available online.

Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.824893 (text/html)
Access to full text is restricted to subscribers.

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:taf:jnlasa:v:108:y:2013:i:504:p:1269-1283

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2013.824893

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().