@article{2bc28080936041348836a2f95e306d86,
title = "Maximum likelihood estimation in linear models with a Gaussian model matrix",
abstract = "We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a Gaussian model matrix. We derive the maximum likelihood (ML) estimator for this problem and show that it can be found using a simple line-search over a unimodal function that can be efficiently evaluated. We then discuss the similarity between the ML, the total least squares (TLS), the regularized TLS, and the expected least squares estimators.",
keywords = "Errors in variables (EIV), Linear models, Maximum likelihood (ML) estimation, Random model matrix, Total least squares (TLS)",
author = "Ami Wiesel and Eldar, {Yonica C.} and Amir Beck",
note = "Funding Information: Manuscript received September 7, 2005; revised December 5, 2005. This work was supported in part by the Israel Science Foundation and in part by the European Union 6th Framework Program, via the NEWCOM network of excellence. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Brian Sadler.",
year = "2006",
month = may,
doi = "10.1109/LSP.2006.870377",
language = "אנגלית",
volume = "13",
pages = "292--295",
journal = "IEEE Signal Processing Letters",
issn = "1070-9908",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",
}