Confidence balls in Gaussian regression
Statistics Theory
2007-06-13 v1 Statistics Theory
Abstract
Starting from the observation of an R^n-Gaussian vector of mean f and covariance matrix \sigma^2 I_n (I_n is the identity matrix), we propose a method for building a Euclidean confidence ball around f, with prescribed probability of coverage. For each n, we describe its nonasymptotic property and show its optimality with respect to some criteria.
Keywords
Cite
@article{arxiv.math/0406425,
title = {Confidence balls in Gaussian regression},
author = {Yannick Baraud},
journal= {arXiv preprint arXiv:math/0406425},
year = {2007}
}