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      On the overestimation of the largest eigenvalue of a covariance matrix

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          Abstract

          In this paper, we use a new approach to prove that the largest eigenvalue of the sample covariance matrix of a normally distributed vector is bigger than the true largest eigenvalue with probability 1 when the dimension is infinite. We prove a similar result for the smallest eigenvalue.

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          Author and article information

          Journal
          11 August 2017
          Article
          1708.03551

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          15 pages, 1 figure
          math.PR math.ST q-fin.MF stat.TH

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