@techreport{Cizek2004Numerical,
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.},
author = {Pavel \v{C}\'{\i}\v{z}ek and Lenka \v{C}\'{\i}\v{z}kov\'{a}},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330},
language = {eng},
number = {2004,23},
title = {Numerical Linear Algebra},
type = {Papers / Humboldt-Universit\"{a}t Berlin, Center for Applied Statistics and Economics (CASE)},
url = {http://hdl.handle.net/10419/22197},
year = {2004}
}