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Empirical Bayes linear regression with unknown model order
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
2007 (engelsk)Inngår i: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol III, Pts 1-3, Proceedings, 2007, s. 773-776Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2007. s. 773-776
Serie
International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149 ; 3
Emneord [en]
linear systems, Bayes procedures, modeling, least mean square methods, parameter estimation
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-18253DOI: 10.1109/ICASSP.2007.366794ISI: 000248906600194ISBN: 1-4244-0727-3 (tryckt)OAI: oai:DiVA.org:uu-18253DiVA, id: diva2:46025
Konferanse
32nd IEEE International Conference on Acoustics, Speech and Signal Processing Honolulu, HI, APR 15-20, 2007
Tilgjengelig fra: 2006-12-20 Laget: 2006-12-20 Sist oppdatert: 2011-04-14bibliografisk kontrollert

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Totalt: 470 treff
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  • de-DE
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  • sv-SE
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