USING NONSTOCHASTIC TERMS TO ADVANTAGE IN KERNEL-BASED ESTIMATION OF INTEGRATED SQUARED DENSITY DERIVATIVES

被引：108

作者：

JONES, MC

SHEATHER, SJ

机构：

[1] OPEN UNIV,DEPT STAT,MILTON KEYNES MK7 6AA,BUCKS,ENGLAND

[2] UNIV NEW S WALES,AUSTRALIAN GRAD SCH MANAGEMENT,KENSINGTON,NSW 2033,AUSTRALIA

来源：

STATISTICS & PROBABILITY LETTERS | 1991年 / 11卷 / 06期

关键词：

BANDWIDTH SELECTION; BIAS REDUCTION; FUNCTIONAL ESTIMATION; KERNEL DENSITY ESTIMATION; RATES OF CONVERGENCE; SMOOTHING;

D O I：

10.1016/0167-7152(91)90116-9

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：

Improved kernel-based estimates of integrated squared density derivatives are obtained by reinstating non-stochastic terms that have previously been omitted, and using the bandwidth to (approximately) cancel these positive quantities with the leading smoothing bias terms which are negative. Such estimators have exhibited great practical merit in the context of data-based selection of the bandwidth in kernel density estimation, a motivating application of this work discussed elsewhere.

引用

页码：511 / 514

页数：4

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