Nearly Optimal Robust Mean Estimation via Empirical Characteristic Function

04/05/2020
by   Sohail Bahmani, et al.
0

We propose an estimator for the mean of random variables in separable real Banach spaces using the empirical characteristic function. Assuming that the covariance operator of the random variable is bounded in a precise sense, we show that the proposed estimator achieves a nearly optimal rate. Furthermore, we show robustness of the estimator against adversarial contamination.

READ FULL TEXT
research
07/26/2019

Robust multivariate mean estimation: the optimality of trimmed mean

We consider the problem of estimating the mean of a random vector based ...
research
07/26/2022

Asymptotic normality of an estimator of kernel-based conditional mean dependence measure

We propose an estimator of the kernel-based conditional mean dependence ...
research
08/27/2021

Quantum Sub-Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-val...
research
06/25/2019

Distribution-robust mean estimation via smoothed random perturbations

We consider the problem of mean estimation assuming only finite variance...
research
06/16/2018

Near-optimal mean estimators with respect to general norms

We study the problem of estimating the mean of a random vector in R^d ba...
research
11/06/2018

Robust multiple-set linear canonical analysis based on minimum covariance determinant estimator

By deriving influence functions related to multiple-set linear canonical...
research
03/27/2019

On the Adversarial Robustness of Multivariate Robust Estimation

In this paper, we investigate the adversarial robustness of multivariate...

Please sign up or login with your details

Forgot password? Click here to reset