Abstract
Consider the Ordinary Linear Model (OLM)
where A ∈ ℜmxn (m > n) is the exogenous data matrix, y ∈ ℜm is the response vector and ε ∈ ℜm is the noise vector with zero mean and dispersion matrix σ2I m }. The least squares estimator of the parameter vector x ℜm
has an infinite number of solutions when A does not have full rank. However, a unique minimum 2-norm estimator of x say, x, can be computed.
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© 2000 Springer Science+Business Media New York
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Kontoghiorghes, E.J. (2000). Olm Not of Full Rank. In: Parallel Algorithms for Linear Models. Advances in Computational Economics, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4571-2_2
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DOI: https://doi.org/10.1007/978-1-4615-4571-2_2
Publisher Name: Springer, Boston, MA
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