# Confidence intervals for nonparametric regression

arXiv: https://arxiv.org/abs/2203.10643 · HTML: https://mlsanity.com/paper/2203.10643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Generalization bounds for nonparametric regression with β-mixing samples](https://mlsanity.com/paper/2108.00997/) (2021)

## Abstract (arXiv)

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 L2-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.