Functional Logistic Discrimination Via Regularized Basis Expansions

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.