相依函数型数据条件密度估计的渐近性质被引量：1

Asymptotic Properties of Conditional Density Estimation for Dependence Functional Data

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摘要利用Kolmogorov熵的方法研究了基于相依函数型数据条件密度函数的非参数估计,在一定的条件下建立了条件密度函数双重核估计量的几乎完全一致收敛速度及估计量的渐近分布,推广了现有文献中相关结果. In this paper, we investigate the almost uniform complete convergence of nonpara- metric conditional density estimation for functional data by the Kolmogorov＇s entropy in some semi-metric functional space and obtain the uniform almost complete convergence rate of the estimator under dependent case, which extend the i.i.d, functional data to the dependence setting. At the same time, the asymptotic normal of the estimator is also established in this case.

作者凌能祥丁洁

机构地区合肥工业大学数学学院

出处《数学物理学报（A辑）》 CSCD 北大核心 2012年第3期547-556,共10页 Acta Mathematica Scientia

基金国家自然科学基金(11171001) 教育部人文社科规划基金(10YJA910005) 教育部科学技术研究重大项目(309017) 安徽省自然科学基金(11040606M03) 安徽省教育厅重点科研基金项目(KJ2010A282)资助

关键词条件密度函数相依函数型数据熵几乎完全一致收敛速度渐近正态性 Dependence functional data Entropy Rate of uniform almost complete convergence Conditional density.

分类号 O212.7 [理学—概率论与数理统计]

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