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Empirical Bayes Linear Regression with Unknown Model Order
Uppsala University.
Royal Institute of Technology (KTH).ORCID iD: 0000-0002-7599-4367
2007 (English)In: Proceedings of the 32nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'07), 2007, III-773-III-776 p.Conference paper, Published paper (Refereed)
Abstract [en]

We study the maximum a posteriori probability model order selection algorithm for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model, we also derive the minimum mean-square error coefficient vector estimate. The approaches are denoted BOSS (Bayesian order selection strategy) and BPM (Bayesian parameter estimation method), respectively. Both BOSS and BPM require a priori knowledge on the distribution of the coefficients. However, under the assumption that the coefficient variance profile is smooth, we derive "empirical Bayesian" versions of our algorithms, which require little or no information from the user. We show in numerical examples that the estimators can outperform several classical methods, including the well-known AIC and BIC for order selection.

Place, publisher, year, edition, pages
2007. III-773-III-776 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-42501DOI: 10.1109/ICASSP.2007.366794Local ID: 65143ISBN: 1-4244-0727-3 (print)ISBN: 1-4244-0728-1 (print)OAI: oai:DiVA.org:liu-42501DiVA: diva2:263358
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-08-31Bibliographically approved

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Larsson, Erik G.

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