Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation

被引：42

作者：

Duong, T ^{[1
]}

Hazelton, ML ^{[1
]}

机构：

[1] Univ Western Australia, Sch Math & Stat, Crawley, WA 6009, Australia

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2005年 / 93卷 / 02期

关键词：

asymptotic; biased cross-validation; Gaussian kernel; MISE; plug-in; smoothing;

D O I：

10.1016/j.jmva.2004.04.004

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：

Progress in selection of smoothing parameters for kernel density estimation has been much slower in the multivariate than univariate setting. Within the context of multivariate density estimation attention has focused on diagonal bandwidth matrices. However, there is evidence to suggest that the use of full (or unconstrained) bandwidth matrices can be beneficial. This paper presents some results in the asymptotic analysis of data-driven selectors of full bandwidth matrices. In particular, we give relative rates of convergence for plug-in selectors and a biased cross-validation selector. (c) 2004 Elsevier Inc. All rights reserved.

引用

页码：417 / 433

页数：17

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