Asymptotic distribution of data-driven smoothers in density and regression estimation under dependence

被引：9

作者：

Boente, G

Fraiman, R

机构：

[1] UNIV BUENOS AIRES,DEPT MATEMAT,BUENOS AIRES,DF,ARGENTINA

[2] UNIV REPUBL ORIENTAL URUGUAY,FAC CIENCIAS,CTR MATEMAT,MONTEVIDEO,URUGUAY

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 1995年 / 23卷 / 04期

关键词：

data-driven bandwidth selectors; kernel estimates; density estimation; nonparametric regression; autoregression models; alpha-mixing processes;

D O I：

10.2307/3315382

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：

We consider automatic data-driven density, regression and autoregression estimates, based on any random bandwidth selector (h) over cap(T). We show that in a first-order asymptotic approximation they behave as well as the related estimates obtained with the ''optimal'' bandwidth h(T) as long as (h) over cap(T)/h(T) --> 1 in probability. The results are obtained for dependent observations; some of them are also new for independent observations.

引用

页码：383 / 397

页数：15

共 60 条

[1] ON BANDWIDTH VARIATION IN KERNEL ESTIMATES - A SQUARE ROOT LAW [J].