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2008

Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds

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Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds
In this paper, we consider the problem of estimating an unknown deterministic parameter vector in a linear regression model with random Gaussian uncertainty in the mixing matrix. We prove that the maximum-likelihood (ML) estimator is a (de)regularized least squares estimator and develop three alternative approaches for finding the regularization parameter that maximizes the likelihood. We analyze the performance using the Cram
Ami Wiesel, Yonina C. Eldar, Arie Yeredor
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2008
Where TSP
Authors Ami Wiesel, Yonina C. Eldar, Arie Yeredor
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