| Paper: | SPTM-P3.1 | ||
| Session: | System Modeling, Representation and Identification | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: System Modeling, Representation, and Identification | ||
| Title: | Linear Regression With a Sparse Parameter Vector | ||
| Authors: | Erik G. Larsson, Royal Institute of Technology (KTH), Sweden; Yngve Selén, Uppsala University, Sweden | ||
| Abstract: | 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. | ||
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