摘要
本文在混合样本下讨论Priestley和CHAO(1972)提出的一类非参数核回归估计的渐近性质,在较弱的条件下证明了该估计的完全收敛性与强相合性.
For -mixing samples, we discuss the asymptotic properties of the nonparametric kernel regression estimator proposed by Priestley and Chao（1972）. Under more weaker conditions, its complete convergence and strong consistency are proved.
作者
杨秀桃
杨善朝
YANG Xiutao;YANG Shanchao(Natural science Teaching Department, Haikou college of Economics, Haikou 571127, China;School of Mathematics and Statistics, Guangxi Normal University, Guilin 5~100~, China)
出处
《应用数学》
CSCD
北大核心
2018年第2期422-428,共7页
Mathematica Applicata
基金
海南省自然科学基金(117173)
国家自然科学基金(11061007)
关键词
混合样本
非参数核回归估计
完全收敛性
强相合性
p-mixing sample
Nonparametric kernel regression estimator
Complete convergenceStrong consistency
