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

页码：519 / 522

页数：4

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