摘要
设f_n是基于一个核函数K和取值于R^d的独立同分布随机变量列的一个非参数核密度估计.本文证明了{f_n(x)-f_n(-x),n≥1}在L_1(R^d)空间下的两个中偏差定理.
Let fn be a non-parametric kernel density estimator based on a kernel function K and a sequence of independent and identically distributed random variables taking values in Rd. In this paper we prove two moderate deviation theorems in L1(Rd) for {fn(x)-fn(x), n≥1}.
作者
徐明周
丁云正
周永正
XU Mingzhou;DING Yunzheng;ZHOU Yongzheng(School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China)
出处
《应用概率统计》
CSCD
北大核心
2019年第2期141-152,共12页
Chinese Journal of Applied Probability and Statistics
基金
supported by the Scientific Program of Department of Education of Jiangxi Province of China(Grant Nos.GJJ150894
GJJ150905)
关键词
对称检验
核密度估计
中偏差
symmetry test
kernel density estimator
moderate deviations
