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An approach to sparse model selection and averaging
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2006 (English)In: Conference Record of the 2006 IEEE Instrumentation and Measurement Technology Conference (IMTC 2006): Sorrento, Italy 24-27 April 2006, 2006Conference paper, Published paper (Refereed)
Abstract [en]

Parameter estimation when the true model structure is unknown is a commonly occurring task in measurement problems. In a sparse modeling scenario, the number of possible models grows exponentially with the total number of parameters. The full set of models therefore becomes computationally infeasible to handle. We propose a method, based on successive model reduction, for finding a sound and computationally feasible set of sparse linear regression models. Once this set of models has been found, standard model selection or model averaging techniques can be applied. We demonstrate the performance of our method by some numerical examples.

Place, publisher, year, edition, pages
2006.
Keyword [en]
linear systems, model reduction, channel measurement, least squares estimation, parameter estimation, signal processing, system identification
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-79396OAI: oai:DiVA.org:uu-79396DiVA: diva2:107309
Available from: 2006-12-20 Created: 2006-12-20

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Selén, YngveGudmundson, ErikStoica, Peter

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