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α-混合相依函数型数据改良核回归估计的渐近正态性 被引量:2

Asymptotic normality of modified kernel regression estimator for α-mixing functional data
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摘要 文章对回归模型Y=r(X)+ε进行了研究。设{(Xi,Yi),1≤i≤n}为取值于E×R上的一组同分布样本,其中E是由半度量d(.,.)生成的某个抽象的半度量空间,R是一个实数空间。在α-混合相依情形下,利用Bernstein大块小块过程,建立函数型数据的改良核回归估计的渐近正态性。 In this paper, the regression model Y=r(X)+ε is investigated. { (Xi ,Yi), 1≤i≤n} is set as the random vectors assumed to be identically distributed values taken in EXR, where E is a certain abstract semi-metric space endowed with a semi-metric d( · , ·) and R is a real space. The asymptotic normality of a modified kernel regression estimator for α-mixing functional data is established by employing Bernstein's big-block and small-block procedure.
出处 《合肥工业大学学报(自然科学版)》 CAS CSCD 北大核心 2011年第10期1584-1587,共4页 Journal of Hefei University of Technology:Natural Science
关键词 函数型数据 改良的核回归估计 Α-混合 渐近正态性 functional data modified kernel regression estimator α-mixing asymptotic normality
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