  We seek to improve estimates of the power spectrum covariance matrix from a
limited number of simulations by employing a novel statistical technique known
as shrinkage estimation. The shrinkage technique optimally combines an
empirical estimate of the covariance with a model (the target) to minimize the
total mean squared error compared to the true underlying covariance. We test
this technique on N-body simulations and evaluate its performance by estimating
cosmological parameters. Using a simple diagonal target, we show that the
shrinkage estimator significantly outperforms both the empirical covariance and
the target individually when using a small number of simulations. We find that
reducing noise in the covariance estimate is essential for properly estimating
the values of cosmological parameters as well as their confidence intervals. We
extend our method to the jackknife covariance estimator and again find
significant improvement, though simulations give better results. Even for
thousands of simulations we still find evidence that our method improves
estimation of the covariance matrix. Because our method is simple, requires
negligible additional numerical effort, and produces superior results, we
always advocate shrinkage estimation for the covariance of the power spectrum
and other large-scale structure measurements when purely theoretical modeling
of the covariance is insufficient.
