Optimal recursive estimation of raw data

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dc.contributor.authorTorokhti, A.
dc.contributor.authorHowlett, P.
dc.contributor.authorPearce, C.
dc.date.issued2005
dc.descriptionThe original publication is available at www.springerlink.com
dc.description.abstractWe present a new approach to the optimal estimation of random vectors. The approach is based on a combination of a specific iterative procedure and the solution of a best approximation problem with a polynomial approximant. We show that the combination of these new techniques allow us to build a computationally effective and flexible estimator. The strict justification of the proposed technique is provided.
dc.description.statementofresponsibilityAnatoli Torokhti, Phil Howlett and Charles Pearce
dc.identifier.citationAnnals of Operations Research, 2005; 133(1-3):285-302
dc.identifier.doi10.1007/s10479-004-5039-5
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.urihttp://hdl.handle.net/2440/17833
dc.language.isoen
dc.publisherKluwer Academic Publishers
dc.source.urihttp://www.springerlink.com/content/gg5p715557g16342/
dc.subjecterror minimization
dc.subjectstochastic vector
dc.subjectoptimal estimate
dc.titleOptimal recursive estimation of raw data
dc.typeJournal article
pubs.publication-statusPublished

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