k-Nearest Neighbour method in functional nonparametric regression

被引：127

作者：

Burba, Florent ^{[1
]}

Ferraty, Frederic ^{[1
]}

Vieu, Philippe ^{[1
]}

机构：

[1] Univ Toulouse 3, IMT, F-31062 Toulouse, France

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2009年 / 21卷 / 04期

关键词：

functional data; nonparametric regression; kNN estimator; rate of convergence; random bandwidth; CONVERGENCE;

D O I：

10.1080/10485250802668909

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 article is to study the k-nearest neighbour (kNN) method in nonparametric functional regression. We present asymptotic properties of the kNN kernel estimator: the almost-complete convergence and its rate. Then, we illustrate the effectiveness of this method by comparing it with the traditional kernel approach first on simulated datasets and then on a real chemometrical example. We also present in this article an important technical tool which could be useful in many other situations than ours.

引用

页码：453 / 469

页数：17

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