تقرير
Confidence intervals for nonparametric regression
العنوان: | Confidence intervals for nonparametric regression |
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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. |