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

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Ami Wiesel, Yonina C. Eldar, Arie Yeredor
• Keywords: Linear Regression, Linear Regression Model, TSP 2008, Unknown Deterministic Parameter