MULTIVARIATE DATA-DRIVEN K-NN FUNCTION ESTIMATION

被引：1
|
作者：
BHATTACHARYA, PK
MACK, YP
机构：
[1] Division of Statistics, University of California, Davis
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 1990年 / 35卷 / 01期
基金：
美国国家科学基金会;
关键词：
bandwidth; density estimation; induced order statistics; kernel; nearest neighbor; regression estimation; weak convergence;
D O I：
10.1016/0047-259X(90)90012-7
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：
In Bhattacharya and Mack (Ann. Statist. 15 (1987), 976-994), it was shown (among other things) that adapting for the optimal choice of k in univariate k-nearest neighbor density and regression estimation is feasible using weak convergence techniques. We now show that the same holds true for the multivariate case. Our results parallel Krieger and Pickands (Ann. Statist. 9 (1981), 1066-1078) and Mack and Müller (J. Multivariate Anal. 23 (1987), 169-182) for adaptive multivariate kernel density, respectively, regression, estimation. © 1990.
引用
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页码：1 / 11
页数：11
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