Mean estimation with sub-Gaussian rates in polynomial time

ANNALS OF STATISTICS, pp. 1193-1213, 2020.

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Abstract:

We study polynomial time algorithms for estimating the mean of a heavy-tailed multivariate random vector. We assume only that the random vector X has finite mean and covariance. In this setting, the radius of confidence intervals achieved by the empirical mean are large compared to the case that X is Gaussian or sub-Gaussian. We offer the...More

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