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Linear regression with a sparse parameter vector
Royal Institute of Technology.ORCID iD: 0000-0002-7599-4367
Uppsala University.
2007 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, 451-460 p.Article in journal (Refereed) Published
Abstract [en]

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) estimate of the parameter vector and a computationally efficient approximation of it. We also derive an empirical-Bayesian version of the estimator, which does not need any a priori information, nor does it need the selection of any user parameters. As a byproduct, we obtain a powerful model ("basis") selection tool for sparse models. The performance and robustness of our new estimators are illustrated via numerical examples.

Place, publisher, year, edition, pages
2007. Vol. 55, 451-460 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-42480DOI: 10.1109/TSP.2006.887109Local ID: 64959OAI: oai:DiVA.org:liu-42480DiVA: diva2:263337
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-08-31Bibliographically approved

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Larsson, Erik

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