Functional Logistic Discrimination Via Regularized Basis Expansions

Yuko Araki, Sadanori Konishi, Shuichi Kawano and Hidetoshi Matsui

Communications in Statistics - Theory and Methods, 2009, vol. 38, issue 16-17, 2944-2957

Abstract: We introduce a functional logistic discrimination based on basis expansions with the help of regularization, which classifies functional data into several distinct groups. A crucial issue in model building process is the choice of regularization parameters. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a Bayesian model selection criterion for evaluating models estimated by the method of regularization in the context of functional logistic discrimination. Monte Carlo experiments are conducted to examine the efficiency of the proposed functional discrimination procedure. We also apply our procedure to the analysis of yeast cell cycle microarray data. The results show that our modeling procedure provides useful tools for classifying functions or curves.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610920902947246 (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:lstaxx:v:38:y:2009:i:16-17:p:2944-2957

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610920902947246

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().