Functional logistic discrimination via regularized basis expansions

Yuko Araki, Sadanori Konishi, Shuichi Kawano, Hidetoshi Matsui

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2944-2957
Number of pages14
JournalCommunications in Statistics - Theory and Methods
Volume38
Issue number16-17
DOIs
Publication statusPublished - Jan 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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