On cross-validation in kernel and partitioning regression estimation.

DOI10.1016/S0167-7152(02)00103-7zbMath1092.62530MaRDI QIDQ1871263

Harro Walk

Publication date: 7 May 2003

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

Cross-validationBest deterministic parameter choiceCubic partitionerror (MISE)Mean integrated squaredNaive kernelRegression estimate

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items (3)

Kernel regression estimation for incomplete data with applications ⋮ On the \(L_p\) norms of kernel regression estimators for incomplete data with applications to classification ⋮ A General Likelihood Framework for Characterizing the Time Course of Neural Activity

Cites Work

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• Optimal bandwidth selection in nonparametric regression function estimation
• An Efron-Stein inequality for nonsymmetric statistics
• Random approximations to some measures of accuracy in nonparametric curve estimation
• The jackknife estimate of variance
• An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
• A bound on the expected maximal deviation of averages from their means.
• Optimal global rates of convergence for nonparametric regression
• A distribution-free theory of nonparametric regression
• Smoothing methods in statistics
• Asymptotic properties of integrated square error and cross-validation for kernel estimation of a regression function

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