Functional principal component regression with noisy covariate

被引：0
|
作者：
Crambes, Christophe [1 ]
机构：
[1] Univ Toulouse 3, Lab Stat & Probabil, F-31062 Toulouse, France
来源：
COMPTES RENDUS MATHEMATIQUE | 2007年 / 345卷 / 09期
关键词：
D O I：
10.1016/j.crma.2007.10.015
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This Note deals with a study of the functional linear model when the covariate is noisy. We smooth each noisy curve using a kernel smoothing method, and then a functional principal component regression is done. We present the estimation procedure of the functional coefficient of the model, as well as a convergence result of the estimator.
引用
收藏
页码：519 / 522
页数：4
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