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Title: All-in-one Robust Estimator of the Gaussian Mean Authors:  Arshak Minasyan - ENSAE (France) [presenting]
Arnak Dalayan - CREST, ENSAE, IP Paris (France)
Abstract: The goal is to show that a single robust estimator of the mean of a multivariate Gaussian distribution can enjoy five desirable properties. First, it is computationally tractable in the sense that it can be computed in a time, which is at most polynomial in dimension, sample size, and the logarithm of the inverse of the contamination rate. Second, it is equivariant translations, uniform scaling, and orthogonal transformations. Third, it has a high breakdown point equal to 0.5, and a nearly minimax rate breakdownpoint approximately equal to 0.28. Fourth, it is minimax rate optimal, up to a logarithmic factor, when data consists of independent observations corrupted by adversarially chosen outliers. Fifth, it is asymptotically efficient when the rate of contamination tends to zero. The estimator is obtained by an iterative reweighting approach. Each sample point is assigned a weight that is iteratively updated by solving a convex optimization problem. We also establish dimension-free nonasymptotic risk bound for the expected error of the proposed estimator. It is the first result of this kind in the literature and involves only the effective rank of the covariance matrix. Finally, we show that the obtained results can be extended to sub-Gaussian distributions, as well as to the cases of the unknown rate of contamination or unknown covariance matrix.