Abstract
Over the last few years many methods have been developed for analyzing functional data with different objectives. The purpose of this paper is to predict a binary response variable in terms of a functional variable whose sample information is given by a set of curves measured without error. In order to solve this problem we formulate a functional logistic regression model and propose its estimation by approximating the sample paths in a finite dimensional space generated by a basis. Then, the problem is reduced to a multiple logistic regression model with highly correlated covariates. In order to reduce dimension and to avoid multicollinearity, two different approaches of functional principal component analysis of the sample paths are proposed. Finally, a simulation study for evaluating the estimating performance of the proposed principal component approaches is developed.
Keywords
MulticollinearityFunctional principal component analysisPrincipal component analysisMathematicsCovariateLogistic regressionFunctional data analysisPrincipal component regressionStatisticsDimension (graph theory)Sample spaceRegression analysisComponent (thermodynamics)Variable (mathematics)Sample (material)Econometrics
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Publication Info
- Year
- 2004
- Type
- article
- Volume
- 16
- Issue
- 3-4
- Pages
- 365-384
- Citations
- 130
- Access
- Closed
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Cite This
Manuel Escabias,
Ana M. Aguilera,
Mariano J. Valderrama
(2004).
Principal component estimation of functional logistic regression: discussion of two different approaches.
Journal of nonparametric statistics
, 16
(3-4)
, 365-384.
https://doi.org/10.1080/10485250310001624738
Identifiers
- DOI
- 10.1080/10485250310001624738