PRESMOOTHING IN FUNCTIONAL LINEAR REGRESSION

被引：38
|
作者：
Ferraty, Frederic [1 ]
Gonzalez-Manteiga, Wenceslao [2 ,3 ]
Martinez-Calvo, Adela [2 ,3 ]
Vieu, Philippe [1 ]
机构：
[1] Univ Toulouse 3, Inst Math, Lab Stat & Probabilites, F-31062 Toulouse, France
[2] Univ Santiago de Compostela, Dept Estat, Santiago De Compostela 15782, Spain
[3] Univ Santiago de Compostela, Fac Matemat, IO, Santiago De Compostela 15782, Spain
来源：
STATISTICA SINICA | 2012年 / 22卷 / 01期
关键词：
Functional linear regression (FLR); functional principal components analysis (FPCA); nonparametric kernel estimator; presmoothing; ESTIMATORS;
D O I：
10.5705/ss.2010.085
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：
In this paper, we consider the functional linear model with scalar response, and explanatory variable valued in a function space. In recent literature, functional principal components analysis (FPCA) has been used to estimate the model parameter. We propose to modify this approach by using presmoothing techniques. For this new estimate, consistency is stated and efficiency by comparison with the standard FPCA estimator is studied. We have also analysed the behaviour of our presmoothed estimator by means of a simulation study and data applications.
引用
收藏
页码：69 / 94
页数：26
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