تقرير
Confidence intervals for nonparametric regression
| العنوان: | Confidence intervals for nonparametric regression |
|---|---|
| المؤلفون: | Barrera, David |
| سنة النشر: | 2022 |
| المجموعة: | Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Statistics Theory, Mathematics - Probability, Statistics - Machine Learning, 62G05, 62G08, 62C99 |
| الوصف: | 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. Comment: 32 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.10643 |
| رقم الأكسشن: | edsarx.2203.10643 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |