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Linear regression with a sparse parameter vector
KTH, School of Electrical Engineering (EES), Communication Theory.
2007 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 2, 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, no 2, 451-460 p.
Keyword [en]
basis selection, Bayesian inference, Lasso linear regression, minimum mean-square error (MMSE) estimation, model averaging, model selection, sparse models, variable selection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-37060DOI: 10.1109/TSP.2006.887109ISI: 000243952600005Scopus ID: 2-s2.0-33847669280OAI: oai:DiVA.org:kth-37060DiVA: diva2:431941
Available from: 2011-07-27 Created: 2011-07-27 Last updated: 2017-12-08Bibliographically approved

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