ASYMPTOTIC BOUNDS FOR THE EXPECTED L(1) ERROR OF A MULTIVARIATE KERNEL DENSITY ESTIMATOR

被引：15
|
作者：
HOLMSTROM, L
KLEMELA, J
机构：
[1] Rolf Nevanlinna Institute, University of Helsinki, Helsinki
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 1992年 / 42卷 / 02期
基金：
芬兰科学院;
关键词：
NONPARAMETRIC DENSITY ESTIMATION; MULTIVARIATE KERNEL ESTIMATOR; L(1) ERROR; DISCRIMINATION; NUMERICAL SIMULATIONS;
D O I：
10.1016/0047-259X(92)90046-I
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 kernel estimator of a multivariate probability density function is studied. An asymptotic upper bound for the expected L1 error of the estimator is derived. An asymptotic lower bound result and a formula for the exact asymptotic error are also given. The goodness of the smoothing parameter value derived by minimizing an explicit upper bound is examined in numerical simulations that consist of two different experiments. First, the L1 error is estimated using numerical integration and, second, the effect of the choice of the smoothing parameter in discrimination tasks is studied. © 1992.
引用
收藏
页码：245 / 266
页数：22
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