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• W2153657340 abstract "We consider estimation of a sparse parameter vector from measurements corrupted by white Gaussian noise. Using the framework of reproducing kernel Hilbert spaces, we derive closed-form expressions of the Barankin bound, i.e., of the minimum locally achievable variance of any estimator with a prescribed bias function, including the unbiased case. We also derive the locally minimum variance (LMV) estimator that achieves the minimum variance, and a necessary and sufficient condition on the prescribed bias function for the existence of finite-variance estimators and, simultaneously, of the LMV estimator. Finally, we present a numerical comparison of the variance of the hard-thresholding estimator with the corresponding minimum achievable variance." @default.
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• W2153657340 date "2011-07-01" @default.
• W2153657340 modified "2023-09-23" @default.
• W2153657340 title "Minimum variance estimation for the sparse signal in noise model" @default.
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• W2153657340 doi "https://doi.org/10.1109/isit.2011.6033735" @default.
• W2153657340 hasPublicationYear "2011" @default.
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