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Performance Bounds for Sparse Parametric Covariance Estimation in Gaussian Models

20 January 2011
A. Jung
Sebastian Schmutzhard
F. Hlawatsch
Alfred Hero
ArXiv (abs)PDFHTML
Abstract

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).

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