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On deep learning as a remedy for the curse of dimensionality in nonparametric regression. (English) Zbl 1421.62036

This paper generalized prior results about the rate of convergence of suitable multilayer neural network regression estimates when the regression function satisfies a \((p,C)\)-smooth generalized hierarchical interaction model of given order \(d^{*}\) and given level \(l\). The results presented are stronger and more general than the results previously known in the literature because they depend on fewer assumptions in the sense that the convergence rate is obtained with much less rigid assumptions on the functional class the regression function belongs to.

MSC:

62G08 Nonparametric regression and quantile regression
62G20 Asymptotic properties of nonparametric inference
65C60 Computational problems in statistics (MSC2010)
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References:

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