Performance Bounds for Sparse Parametric Covariance Estimation in   Gaussian Models
  We consider estimation of a sparse parameter vector that determines the covariance matrix of a Gaussian random vector via a sparse expansion into known "basis matrices". Using the theory of reproducing kernel Hilbert spaces, we derive lower bounds on the variance of estimators with a given mean function. This includes unbiased estimation as a special case. We also present a numerical comparison of our lower bounds with the variance of two standard estimators (hard-thresholding estimator and maximum likelihood estimator). 
Alexander Jung, Sebastian Schmutzhard, Franz Hlawatsch, Alfred O. Hero   III
cs.IT math.IT math.ST stat.TH