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arXiv · 2203.10643

Confidence intervals for nonparametric regression

David Barrera
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Abstract

We demonstrate and discuss nonasymptotic bounds in probability for the cost of a regression scheme with a general loss function from the perspective of the Rademacher theory, and for the optimality with respect to the average $L^{2}$-distance to the underlying conditional expectations of least squares regression outcomes from the perspective of the Vapnik-Chervonenkis theory. The results follow from an analysis involving independent but possibly nonstationary training samples and can be extended, in a manner that we explain and illustrate, to relevant cases in which the training sample exhibits dependence.

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David Barrera. 2022-03-20. Confidence intervals for nonparametric regression. https://arxiv.org/abs/2203.10643

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