A note on functional linear regression

作者：Baillo Amparo^*

来源：Journal of Statistical Computation and Simulation, 2009, 79(5): 657-669.

DOI：10.1080/00949650701836765

• functional linear regression
• regularization
• covariance eigenfunction
• Fourier basis
• functional principal components
• cross-validation
• roughness penalty

This work focuses on the linear regression model with functional covariate and scalar response. We compare the performance of two (parametric) linear regression estimators and a nonparametric (kernel) estimator via a Monte Carlo simulation study and the analysis of two real data sets. The first linear estimator expands the predictor and the regression weight function in terms of the trigonometric basis, while the second one uses functional principal components. The choice of the regularization degree in the linear estimators is addressed.

• 出版日期2009