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dc.contributor.author
Cernuschi Frias, Bruno
dc.contributor.author
Gama, Fernando
dc.contributor.author
Casaglia, Daniel
dc.date.available
2016-01-06T15:46:20Z
dc.date.issued
2015-02
dc.identifier.citation
Cernuschi Frias, Bruno; Gama, Fernando; Casaglia, Daniel; Analysis and Comparison of Biased Affine Estimators; Institute Of Electrical And Electronics Engineers; Ieee Transactions On Signal Processing; 63; 4; 2-2015; 859-869
dc.identifier.issn
1053-587X
dc.identifier.uri
http://hdl.handle.net/11336/3379
dc.description.abstract
Affine biased estimation is particularly useful when there is some a-priori knowledge on the parameters that can be exploited in adverse situations (when the number of samples is low, or the noise is high). Three different affine estimation strategies are discussed, namely the Deepest Minimum Criterion (DMC), the Min-Max (MM), and the Linear Matrix Inequality (LMI) strategies, and closed form expressions are obtained for all of them, for the case when the a priori knowledge is given in the form of ellipsoidal constraints on the parameter space, and when the covariance matrix of the unbiased estimator is constant. A relationship between affine estimation and Bayesian estimation of the mean of a multivariate Gaussian distribution with Gaussian prior is established and it is shown how affine estimation theory can help in the choice of the Gaussian prior distribution.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute Of Electrical And Electronics Engineers
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
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dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Analysis and Comparison of Biased Affine Estimators
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-03-30 10:35:44.97925-03
dc.journal.volume
63
dc.journal.number
4
dc.journal.pagination
859-869
dc.journal.pais
Estados Unidos
dc.journal.ciudad
New York
dc.description.fil
Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemáticas; Argentina
dc.description.fil
Fil: Gama, Fernando. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina
dc.description.fil
Fil: Casaglia, Daniel. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina
dc.journal.title
Ieee Transactions On Signal Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6996046
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