Electronic Journal of Statistics

Estimation of Kullback-Leibler losses for noisy recovery problems within the exponential family

Charles-Alban Deledalle

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We address the question of estimating Kullback-Leibler losses rather than squared losses in recovery problems where the noise is distributed within the exponential family. Inspired by Stein unbiased risk estimator (SURE), we exhibit conditions under which these losses can be unbiasedly estimated or estimated with a controlled bias. Simulations on parameter selection problems in applications to image denoising and variable selection with Gamma and Poisson noises illustrate the interest of Kullback-Leibler losses and the proposed estimators.

Article information

Electron. J. Statist., Volume 11, Number 2 (2017), 3141-3164.

Received: May 2016
First available in Project Euclid: 29 August 2017

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62F10: Point estimation
Secondary: 62J12: Generalized linear models

Stein unbiased risk estimator model selection Kullback-Leibler divergence exponential family

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Deledalle, Charles-Alban. Estimation of Kullback-Leibler losses for noisy recovery problems within the exponential family. Electron. J. Statist. 11 (2017), no. 2, 3141--3164. doi:10.1214/17-EJS1321. https://projecteuclid.org/euclid.ejs/1503972028

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