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dc.contributor.authorČížek, Pavelen_US
dc.contributor.authorČížková, Lenkaen_US
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.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,23en_US
dc.titleNumerical Linear Algebraen_US
dc.typeWorking Paperen_US
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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