Noisy discriminant analysis with boundary assumptions

Sébastien Loustau and Clément Marteau

Journal of Nonparametric Statistics, 2015, vol. 27, issue 4, 425-441

Abstract: We address the problem of smooth discriminant analysis when data are collected from two samples with measurement errors. This problem turns to be an inverse problem and requires a specific treatment. In this context, we investigate consistency rates of convergence using both a margin assumption, and a complexity assumption in terms of entropy. In particular, we concentrate our attention on a boundary condition on the Bayes set and exhibits two distinct scenarii of convergence for the excess risk. For mildly ill-posed inverse problems, fast rates (i.e. faster than ) may occur whereas in the presence of one 'supersmooth' component for measurement errors, the excess risk is a negative power of .

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2015.1067314 (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:gnstxx:v:27:y:2015:i:4:p:425-441

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2015.1067314

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().