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Parameter estimation for reduced-rank multivariate linear regressions in the presence of correlated noise
KTH, Superseded Departments, Signals, Sensors and Systems.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
2003 (English)In: Proceedings Asilomar Conference on Signals, Systems & Computers, 2003, 2101-2105 p.Conference paper (Refereed)
Abstract [en]

This paper considers the problem of estimating the parameters in a reduced-rank multivariate linear regression. Reduced rank linear regression has applications in areas such as econometrics, statistics and signal processing. The proposed method can accommodate noise with both temporal and spatial correlation. It relies on a weighted low rank approximation of the full rank regression matrix obtained from a least squares fit to the data. Numerical studies suggest performance comparable to the maximum likelihood solution proposed in [1] for the white noise case, and an improvement when the noise is temporally correlated.

Place, publisher, year, edition, pages
2003. 2101-2105 p.
Keyword [en]
Array signal processing, Econometrics, Least squares approximation, Linear regression, Maximum likelihood estimation, Noise reduction, Parameter estimation, Sensor systems, State estimation, Statistics
National Category
Telecommunications Signal Processing
URN: urn:nbn:se:kth:diva-82660DOI: 10.1109/ACSSC.2003.1292350OAI: diva2:498463
37th Asilomar Conference on Signals, Systems and Computers Pacific Grove, CA, Nov. 9-12, 2003.
NR 20140805Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2012-02-12Bibliographically approved

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