Robustness of reweighted least squares kernel based regression

DOI10.1016/j.jmva.2009.09.007zbMath1178.62035OpenAlexW2048777337MaRDI QIDQ1049548

Andreas Christmann, Michiel Debruyne, Mia Hubert, Johan A. K. Suykens

Publication date: 12 January 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2009.09.007

zbMATH Keywords

influence functionkernel methodsrobust statisticsreweightingkernel based regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Linear regression; mixed models (62J05) Applications of functional analysis in probability theory and statistics (46N30)

Related Items

Asymmetric least squares support vector machine classifiers, Fast rates of minimum error entropy with heavy-tailed noise, Gradient descent for robust kernel-based regression, Learning rates for the kernel regularized regression with a differentiable strongly convex loss, Kernel-based maximum correntropy criterion with gradient descent method, Primal and dual model representations in kernel-based learning, Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses, Robustness by reweighting for kernel estimators: an overview

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