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Papers, CASE - Center for Applied Statistics and Economics, HU Berlin >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/22197
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Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Čížek, Pavel | | en_US |
| dc.contributor.author | | Čížková, Lenka | | en_US |
| dc.date.accessioned | | 2009-01-29T14:54:18Z | | - |
| dc.date.available | | 2009-01-29T14:54:18Z | | - |
| dc.date.issued | | 2004 | | en_US |
| dc.identifier.uri | | http://hdl.handle.net/10419/22197 | | - |
| dc.description.abstract | | Many 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.iso | | eng | | en_US |
| dc.relation.ispartofseries | | Papers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,23 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.title | | Numerical Linear Algebra | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 495307289 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | - |
| dc.identifier.repec | | RePEc:zbw:caseps:200423 | | - |
| Appears in Collections: | | Papers, CASE - Center for Applied Statistics and Economics, HU Berlin
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