NONPARAMETRIC-ESTIMATION OF A REGRESSION FUNCTION BY DELTA SEQUENCES

被引：4

作者：

ISOGAI, E ^{[1
]}

机构：

[1] NIIGATA UNIV,DEPT MATH,NIIGATA 95021,JAPAN

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 1990年 / 42卷 / 04期

关键词：

NONPARAMETRIC REGRESSION; MEAN SQUARE CONVERGENCE; STRONG CONSISTENCY; DELTA SEQUENCES;

D O I：

10.1007/BF02481145

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：

A somewhat more general class of nonparametric estimators for estimating an unknown regression function g from noisy data is proposed. The regressor is assumed to be defined on the closed interval [0, 1]. This class of estimators is shown to be pointwisely consistent in the mean square sense and with probability one. Further, it turns out that these estimators can be applied to a wide class of noises.

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收藏

页码：699 / 708

页数：10

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