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V-invariant methods, generalised least squares problems, and the Kalman filter

Osborne, Michael; Soderkvist, Inge

Description

An important consideration in solving generalised least squares problems is the dimension of the covariance matrix V. This has the dimension of the data set and is large when the data set is large. In addition the problem can be formulated to have a well

dc.contributor.authorOsborne, Michael
dc.contributor.authorSoderkvist, Inge
dc.date.accessioned2015-12-13T22:49:07Z
dc.date.available2015-12-13T22:49:07Z
dc.identifier.issn1446-8735
dc.identifier.urihttp://hdl.handle.net/1885/80387
dc.description.abstractAn important consideration in solving generalised least squares problems is the dimension of the covariance matrix V. This has the dimension of the data set and is large when the data set is large. In addition the problem can be formulated to have a well
dc.publisherAustralian Mathematical Society
dc.sourceANZIAM Journal
dc.titleV-invariant methods, generalised least squares problems, and the Kalman filter
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume45
dc.date.issued2004
local.identifier.absfor010399 - Numerical and Computational Mathematics not elsewhere classified
local.identifier.ariespublicationMigratedxPub8652
local.type.statusPublished Version
local.contributor.affiliationOsborne, Michael, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationSoderkvist, Inge, Lulea University of Technology
local.bibliographicCitation.startpageC232
local.bibliographicCitation.lastpageC247
dc.date.updated2015-12-11T10:32:39Z
local.identifier.scopusID2-s2.0-35248864330
CollectionsANU Research Publications

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