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dc.contributor.authorČížek, Pavelen_US
dc.contributor.authorČížková, Lenkaen_US
dc.date.accessioned2009-01-29T14:54:18Z-
dc.date.available2009-01-29T14:54:18Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22197-
dc.description.abstractMany methods of computational statistics lead to matrix-algebra or numerical- mathematics problems. For example, the least squares method in linear regression reduces to solving a system of linear equations. The principal components method is based on finding eigenvalues and eigenvectors of a matrix. Nonlinear optimization methods such as Newton?s method often employ the inversion of a Hessian matrix. In all these cases, we need numerical linear algebra.en_US
dc.language.isoengen_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,23en_US
dc.subject.ddc330en_US
dc.titleNumerical Linear Algebraen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495307289en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200423-
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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