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Easy Column Space Tricks

Simo Puntanen (), George P. H. Styan () and Jarkko Isotalo ()
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Simo Puntanen: University of Tampere, School of Information Sciences
George P. H. Styan: McGill University, Department of Mathematics & Statistics
Jarkko Isotalo: University of Tampere, School of Information Sciences

Chapter Chapter 1 in Matrix Tricks for Linear Statistical Models, 2011, pp 57-70 from Springer

Abstract: Abstract The column space of an n × m matrix A, 1.1 $$\mathcal{C} (A_{n \times m}) = \{{\bf y} \in \mathbb{R}^n: \text{there exists}\ {\bf x} \in \mathbb{R}^m \text{such that}\ {\bf y} = {\bf Ax}\}, $$ and, correspondingly, the null space of A, 1.2 $$\mathcal{N} (A_{n \times m}) = \{{\bf x} \in \mathbb{R}^m: {\bf Ax} = 0\}, $$ are in every-day use throughout this book. In this chapter we take a good look at some of their properties, most of them rather elementary. Our experience is that decent steps in linear models are slow to take unless a reasonable set of column space tricks is in the immediate access. Some of the results that we go through are already likely to be in the reader’s toolbox. However, there cannot be harm in repeating these helpful rules and going through their proofs.

Keywords: Parametric Function; Full Column Rank; Column Space; Independent Column; Black Widow (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-3-642-10473-2_2

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