Parameter estimation for reduced-rank multivariate linear regressions in the presence of correlated noise
2003 (English)In: Proceedings Asilomar Conference on Signals, Systems & Computers, 2003, 2101-2105 p.Conference paper (Refereed)
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  for the white noise case, and an improvement when the noise is temporally correlated.
Place, publisher, year, edition, pages
2003. 2101-2105 p.
Array signal processing, Econometrics, Least squares approximation, Linear regression, Maximum likelihood estimation, Noise reduction, Parameter estimation, Sensor systems, State estimation, Statistics
Telecommunications Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-82660DOI: 10.1109/ACSSC.2003.1292350OAI: oai:DiVA.org:kth-82660DiVA: diva2:498463
37th Asilomar Conference on Signals, Systems and Computers Pacific Grove, CA, Nov. 9-12, 2003.
NR 201408052012-02-122012-02-122012-02-12Bibliographically approved