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Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence

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We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses.

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