Central limit theorem for integrated square error of kernel estimators of spherical density
β Scribed by Lincheng Zhao; Chengqing Wu
- Publisher
- SP Science China Press
- Year
- 2001
- Tongue
- English
- Weight
- 406 KB
- Volume
- 44
- Category
- Article
- ISSN
- 1674-7283
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π SIMILAR VOLUMES
In this paper we consider the weighted average square error A,(rc)= (l/n)~=1 {f"(3))f(Xj)}2~(Xj), where f is the common density function of the independent and identically distributed random vectors X~ ..... X,, f, is the kernel estimator based on these vectors and ~z is a weight function. Using U-s
In this paper, we consider the integrated square error Jn = { f n (x) -f(x)} 2 d x; where f is the common density function of the independent and identically distributed random vectors X1; : : : ; Xn and f n is the kernel estimator with a data-dependent bandwidth. Using the approach introduced by Ha
Hall [J. Multivariate Anal. 14 (1984) 1-16; Ann. Statist. 12 (1984) 241 260] established central limit theorems for the integrated square errors of multivariate nonparametric function estimators. In this article, we extend his results and derive central limit theorems for the averaged square errors