ASYMPTOTIC NORMALITY OF A WEIGHTED INTEGRATED SQUARED ERROR OF KERNEL REGRESSION ESTIMATES WITH DATA-DEPENDENT BANDWIDTH

被引：5
|
作者：
LIERO, H [1 ]
机构：
[1] KARL WEIERSTRASS INST MATH,O-1086 BERLIN,GERMANY
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1992年 / 30卷 / 03期
关键词：
ADAPTIVE ESTIMATION; EMPIRICAL PROCESS; KERNEL ESTIMATOR; LIMIT THEOREM FOR THE ISE; NONPARAMETRIC REGRESSION;
D O I：
10.1016/0378-3758(92)90158-O
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：
Let (X, Y) be a bivariate random vector and let r(t) = E(Y\X = t) be the regression function of Y on X that has to be estimated from a sample of i.i.d. random vectors (X1, Y1),...,(X(n), Y(n)) having the same distribution as (X, Y). In the present paper it is shown that the normalized integrated squared error of a kernel estimator with data-driven bandwidth is asymptotically normally distributed.
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页码：307 / 325
页数：19
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