Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods
L. A. Stefanski,
Yichao Wu and
Kyle White
Journal of the American Statistical Association, 2014, vol. 109, issue 506, 574-589
Abstract:
Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based approach to variable selection is developed. After describing the approach in the familiar context of linear regression, we apply it to the problem of variable selection in nonparametric classification, resulting in a new kernel-based classifier with LASSO-like shrinkage and variable-selection properties. Finite-sample performance of the new classification method is studied via simulation and real data examples, and consistency of the method is studied theoretically. Supplementary materials for the article are available online.
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.858630 (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:109:y:2014:i:506:p:574-589
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20
DOI: 10.1080/01621459.2013.858630
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 ().