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Linear Regression with a Sparse Parameter Vector
Communication Theory Laboratory, School of Electrical Engineering, Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-7599-4367
Division of Systems and Control, Department of Information Technology, Uppsala University, Sweden.
2006 (English)In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006, III-309-III-312 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples.

Place, publisher, year, edition, pages
2006. III-309-III-312 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-77239DOI: 10.1109/ICASSP.2006.1660652ISBN: 1-4244-0469-X (print)OAI: oai:DiVA.org:liu-77239DiVA: diva2:525714
Conference
2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 14-19, Toulouse, France
Available from: 2012-05-09 Created: 2012-05-09 Last updated: 2016-08-31Bibliographically approved

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Larsson, Erik G.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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