Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22197 
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dc.contributor.authorČížek, Pavelen
dc.contributor.authorČížková, Lenkaen
dc.date.accessioned2009-01-29T14:54:18Z-
dc.date.available2009-01-29T14:54:18Z-
dc.date.issued2004-
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
dc.language.isoengen
dc.publisher|aHumboldt-Universität zu Berlin, Center for Applied Statistics and Economics (CASE) |cBerlinen
dc.relation.ispartofseries|aPapers |x2004,23en
dc.subject.ddc330en
dc.titleNumerical Linear Algebra-
dc.typeWorking Paperen
dc.identifier.ppn495307289en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:caseps:200423en

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