The Hilbert kernel regression estimate.

DOI10.1006/jmva.1997.1725zbMath1126.62339OpenAlexW2024489202MaRDI QIDQ1264520

Adam Krzyżak, Luc P. Devroye, László Györfi

Publication date: 18 November 1998

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/4678c352e37752c58ab7aef42ee495314988d058

zbMATH Keywords

convergencebandwidth selectionkernel estimatenonparametric estimationregression function estimationNadaraya-Watson estimate

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of functional analysis in probability theory and statistics (46N30)

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Cites Work

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• Distribution-free pointwise consistency of kernel regression estimate
• Distribution-free consistency results in nonparametric discrimination and regression function estimation
• Consistent window estimation in nonparametric regression
• Optimal rates of convergence for nonparametric estimators
• On the almost everywhere convergence of nonparametric regression function estimates
• An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
• Potential function algorithms for pattern recognition learning machines
• Distribution-free consistency of a nonparametric kernel regression estimate and classification
• Estimation Non-paramétrique de la Régression: Revue Bibliographique
• Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
• The rates of convergence of kernel regression estimates and classification rules
• Probability Inequalities for Sums of Bounded Random Variables
• Some Results on the Complete and Almost Sure Convergence of Linear Combinations of Independent Random Variables and Martingale Differences
• Remarks on Non-Parametric Estimates for Density Functions and Regression Curves