Related papers: Confidence intervals for nonparametric regression

Confidence intervals for nonparametric regression

URL: http://arxiv.org/abs/2203.10643v1
Date: Sun, 20 Mar 2022 20:42:00 GMT
Title: Confidence intervals for nonparametric regression
Authors: David Barrera
Abstract summary: We discuss nonasymptotic bounds in probability for the cost of a regression scheme with a general loss function. The results follow from an analysis involving independent but possibly nonstationary training samples.
Score: 1.52292571922932
License: http://creativecommons.org/licenses/by/4.0/
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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