Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22197 
Year of Publication: 
2004
Series/Report no.: 
Papers No. 2004,23
Publisher: 
Humboldt-Universität zu Berlin, Center for Applied Statistics and Economics (CASE), Berlin
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.
Document Type: 
Working Paper

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