Partial estimation of covariance matrices
arXiv:1008.1716
Abstract
A classical approach to accurately estimating the covariance matrix Σof a p-variate normal distribution is to draw a sample of size n > p and form a sample covariance matrix. However, many modern applications operate with much smaller sample sizes, thus calling for estimation guarantees in the regime n << p. We show that a sample of size n = O(m log^6 p) is sufficient to accurately estimate in operator norm an arbitrary symmetric part of Σconsisting of m < n entries per row. This follows from a general result on estimating Hadamard products M.Σ, where M is an arbitrary symmetric matrix.
15 pages, to appear in PTRF. Small changes in light of comments from the referee