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December 2020 Empirical Bayes oracle uncertainty quantification for regression
Eduard Belitser, Subhashis Ghosal
Ann. Statist. 48(6): 3113-3137 (December 2020). DOI: 10.1214/19-AOS1845

Abstract

We propose an empirical Bayes method for high-dimensional linear regression models. Following an oracle approach that quantifies the error locally for each possible value of the parameter, we show that an empirical Bayes posterior contracts at the optimal rate at all parameters and leads to uniform size-optimal credible balls with guaranteed coverage under an “excessive bias restriction” condition. This condition gives rise to a new slicing of the entire space that is suitable for ensuring uniformity in uncertainty quantification. The obtained results immediately lead to optimal contraction and coverage properties for many conceivable classes simultaneously. The results are also extended to high-dimensional additive nonparametric regression models.

Citation

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Eduard Belitser. Subhashis Ghosal. "Empirical Bayes oracle uncertainty quantification for regression." Ann. Statist. 48 (6) 3113 - 3137, December 2020. https://doi.org/10.1214/19-AOS1845

Information

Received: 1 October 2017; Revised: 1 March 2019; Published: December 2020
First available in Project Euclid: 11 December 2020

MathSciNet: MR4185802
Digital Object Identifier: 10.1214/19-AOS1845

Subjects:
Primary: 62C05, 62G15, 62H35
Secondary: 62G99

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.48 • No. 6 • December 2020
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