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Excess-Risk Consistency of Group-hard Thresholding Estimator in Robust Estimation of Gaussian Mean

机译:高斯均值估计的群体硬阈值估计的过度风险一致性

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In this work we introduce the notion of the excess risk in the setup of estimation of the Gaussian mean when the observations are corrupted by outliers. It is known that the sample mean loses its good properties in the presence of outliers [5,6]. In addition, even the sample median is not minimax-rate-optimal in the multivariate setting. The optimal rate of the minimax risk in this setting was established by [1]. However, even these minimax-rate-optimality results do not quantify how fast the risk in the contaminated model approaches the risk in the uncontaminated model when the rate of contamination goes to zero. The present paper does a first step in filling this gap by showing that the group hard thresholding estimator has an excess risk that goes to zero when the corruption rate approaches zero.
机译:在这项工作中,当观察结果被异常值损坏时,我们介绍了高斯意味着估计的估计的过度风险的概念。众所周知,样品是指在异常值存在下失去其良好的性质[5,6]。此外,即使样品中位数也不是多变量设置中的最低限度率。 [1]建立了该设置中最低限度风险的最佳速率。然而,即使是这些最小速率 - 最优性结果也没有量化污染模型风险的速度如何在污染速率归零时接近未污染模型中的风险。本文通过表明组硬阈值估计器具有过多的风险,当腐败率接近零时,该纸张填充该间隙的第一步。

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