Local linear regression for functional predictor and scalar response

被引:100
作者
Baillo, Amparo [1 ]
Grane, Aurea [2 ]
机构
[1] Univ Autonoma Madrid, Dept Anal Econ Econ Cuantitat, E-28049 Madrid, Spain
[2] Univ Carlos III Madrid, Dept Estadist, Getafe Madrid 28903, Spain
关键词
Functional data; Nonparametric smoothing; Local linear regression; Kernel regression; Fourier expansion; Cross-validation; MODELS;
D O I
10.1016/j.jmva.2008.03.008
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The aim of this work is to introduce a new nonparametric regression technique in the context of functional covariate and scalar response. We propose a local linear regression estimator and study its asymptotic behaviour. Its finite-sample performance is compared with a Nadayara-Watson type kernel regression estimator and with the linear regression estimator via a Monte Carlo study and the analysis of two real data sets. In all the scenarios considered, the local linear regression estimator performs better than the kernel one, in the sense that the mean squared prediction error is lower. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:102 / 111
页数:10
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