Nonparametric regression estimation using penalized least squares

被引：43

作者：

Kohler, M ^{[1
]}

Krzyzak, A

机构：

[1] Univ Stuttgart, Inst Math A, D-70569 Stuttgart, Germany

[2] Concordia Univ, Dept Comp Sci, Montreal, PQ H3G 1M8, Canada

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2001年 / 47卷 / 07期

基金：

加拿大自然科学与工程研究理事会;

关键词：

penalized least squares; regression estimate; smoothing splines; strong consistency; Vapnik-Chervonenkis theory;

D O I：

10.1109/18.998089

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present multivariate penalized least squares regression estimates. We use Vapnik-Chervonenkis theory and bounds on the covering numbers to analyze convergence of the estimates. We show strong consistency of the truncated versions of the estimates without any conditions on the underlying distribution.

引用

页码：3054 / 3058

页数：5

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