February 2022 Finite sample properties of parametric MMD estimation: Robustness to misspecification and dependence
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
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Bernoulli 28(1): 181-213 (February 2022). DOI: 10.3150/21-BEJ1338


Many works in statistics aim at designing a universal estimation procedure, that is, an estimator that would converge to the best approximation of the (unknown) data generating distribution in a model, without any assumption on this distribution. This question is of major interest, in particular because the universality property leads to the robustness of the estimator. In this paper, we tackle the problem of universal estimation using a minimum distance estimator presented in (Briol et al. (2019)) based on the Maximum Mean Discrepancy. We show that the estimator is robust to both dependence and to the presence of outliers in the dataset. Finally, we provide a theoretical study of the stochastic gradient descent algorithm used to compute the estimator, and we support our findings with numerical simulations.


We would like to thank Guillaume Lecué (ENSAE Paris) for his helpful comments, Mathieu Gerber (University of Bristol) who fixed a mistake in the constants in Proposition 4.1, and George Wynne (Imperial College) for his very informative comments on the coefficients ϱt. We also would like to thank the anonymous Referees and the Associate Editor for their insightful comments that helped to improve the structure of the paper. All the remaining mistakes are ours.


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Badr-Eddine Chérief-Abdellatif. Pierre Alquier. "Finite sample properties of parametric MMD estimation: Robustness to misspecification and dependence." Bernoulli 28 (1) 181 - 213, February 2022. https://doi.org/10.3150/21-BEJ1338


Received: 1 July 2020; Revised: 1 March 2021; Published: February 2022
First available in Project Euclid: 10 November 2021

MathSciNet: MR4337702
zbMATH: 07467718
Digital Object Identifier: 10.3150/21-BEJ1338

Keywords: kernel methods , minimum distance estimation , RKHS , robust statistics , universal estimation , Weak dependence

Rights: Copyright © 2022 ISI/BS


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Vol.28 • No. 1 • February 2022
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