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- W2114275391 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)." @default.
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- W2114275391 date "2011-05-01" @default.
- W2114275391 modified "2023-09-28" @default.
- W2114275391 title "Performance bounds for sparse parametric covariance estimation in Gaussian models" @default.
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- W2114275391 doi "https://doi.org/10.1109/icassp.2011.5947268" @default.
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