Projections and Projection Matrices

David A. Harville ()
Additional contact information
David A. Harville: IBM T.J. Watson Research Center, Mathematical Sciences Department

Chapter 12 in Matrix Algebra From a Statistician’s Perspective, 1997, pp 161-178 from Springer

Abstract: Abstract Projections and projection matrices, which are introduced and discussed in this chapter, are frequently encountered in discourse on linear statistical models related to the estimation of parameters and to the analysis of variance. Their appearance in such discourse can be attributed to their connection to the so-called least squares problem—one long-standing approach to the estimation of the parameters of a linear statistical model is based on “fitting” the model by least squares. Their connection to the least squares problem is described and discussed in Section 12.4.

Keywords: Linear System; Linear Space; Column Vector; Normal Equation; Orthogonal Complement (search for similar items in EconPapers)
Date: 1997
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-22677-4_12

Ordering information: This item can be ordered from
http://www.springer.com/9780387226774

DOI: 10.1007/0-387-22677-X_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().