A note on functional linear regression

被引：5
|
作者：
Baillo, Amparo [1 ]
机构：
[1] Univ Autonoma Madrid, Dept Anal Econ Econ Cuantitat, Madrid, Spain
来源：
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2009年 / 79卷 / 05期
关键词：
functional linear regression; regularization; covariance eigenfunction; Fourier basis; functional principal components; cross-validation; roughness penalty; CONVERGENCE; MODELS; RATES;
D O I：
10.1080/00949650701836765
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：657 / 669
页数：13
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