Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries

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determinant of the hankel matrix with binomial entries

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15 صفحه اول

Sub-Gaussian mean estimators

We discuss the possibilities and limitations of estimating the mean of a real-valued random variable from independent and identically distributed observations from a nonasymptotic point of view. In particular, we define estimators with a sub-Gaussian behavior even for certain heavy-tailed distributions. We also prove various impossibility results for mean estimators.

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We study the problem of estimating the mean of a random vector X given a sample of N independent, identically distributed points. We introduce a new estimator that achieves a purely sub-Gaussian performance under the only condition that the second moment of X exists. The estimator is based on a novel concept of a multivariate median. 2010 Mathematics Subject Classification: 62J02, 62G08, 60G25.

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Matrix Decompositions Using sub-Gaussian Random Matrices

In recent years, several algorithms which approximate matrix decomposition have been developed. These algorithms are based on metric conservation features for linear spaces of random projection types. We present a new algorithm, which achieves with high probability a rank-r SVD approximation of an n × n matrix and derive an error bound that does not depend on the first r singular values. Althou...

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ژورنال

عنوان ژورنال: The Annals of Statistics

سال: 2018

ISSN: 0090-5364

DOI: 10.1214/17-aos1642