%0 Journal Article
%T Kernel-Based Partial Conditional Mean Dependence
%A Zhentao Tian
%A Zhongzhan Zhang
%J Open Journal of Statistics
%P 294-311
%@ 2161-7198
%D 2025
%I Scientific Research Publishing
%R 10.4236/ojs.2025.153015
%X We introduce the Kernel-based Partial Conditional Mean Dependence, a scalar-valued measure of conditional mean dependence of <math xmlns='http://www.w3.org/1998/Math/MathML'>
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 are random elements from arbitrary separable Hilbert spaces. This measure extends the Kernel-based Conditional Mean Dependence. As the estimator of the measure is developed, the concentration property of the estimator is proved. Numerical results demonstrate the effectiveness of the new dependence measure in the context of dependence testing, highlighting their advantages in capturing nonlinear partial conditional mean dependencies.
%K Partial Conditional Mean Dependence
%K Hilbert Space
%K High Dimension
%K Test of Independence
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=143448