Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods.

TitleVariable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods.
Publication TypeJournal Article
Year of Publication2014
AuthorsStefanski, L A., Yichao Wu, and Kyle White
JournalJ Am Stat Assoc
Volume109
Issue506
Pagination574-589
Date Published2014
ISSN0162-1459
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 paper are available online.

DOI10.1080/01621459.2013.858630
Alternate JournalJ Am Stat Assoc
Original PublicationVariable selection in nonparametric classification via measurement error model selection likelihoods.
PubMed ID24976661
PubMed Central IDPMC4066561
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
T32 HL079896 / HL / NHLBI NIH HHS / United States
Project: