The eigenspectrum for three shrinkage methods is compared to the sample covariance (dashed black line). Linear shrinkage is performed on the jackknife covariance with N_JK = 74, using scalar, block, and matrix shrinkage. In the bottom subplot we show the ratio with respect to the sample covariance. The solid lines represent the mean and the envelopes the spread (i.e. 95% confidence interval) from ten realisations. Scalar shrinkage, with a single value for the shrinkage intensity, produces a covariance matrix that is non-singular and follows the sample covariance eigenspectrum for all eigenmodes, albeit with larger scatter. Block shrinkage is sometimes singular (eigenmodes > 200 are sometimes zero) while matrix shrinkage is always singular (eigenmodes > 180 are always zero). All methods are biased high for small eigenmodes, due to a bias towards high diagonals in the jackknife covariance which are not altered since we shrink towards a Gaussian correlation prediction.