Vector and Matrix Algebra

Karl-Rudolf Koch
Additional contact information
Karl-Rudolf Koch: Institute of Theoretical Geodesy of the University of Bonn

Chapter 1 in Parameter Estimation and Hypothesis Testing in Linear Models, 1999, pp 3-73 from Springer

Abstract: Abstract The statistical inference on parameters will be derived in linear models. A linear relation can be treated in a compact and lucid form by vectors and matrices, so that in the following, the definitions and theorems of linear algebra are introduced which will be needed later. The methods of vector spaces will also be discussed. They allow one to use geometric conceptions even then, when the spaces being used are of higher dimensions than the three-dimensional space we are familiar with. Finally, generalized inverses are discussed, by which one can easily change models of full rank for the estimation of parameters to models which are not full rank.

Keywords: Full Rank; Null Space; Triangular Matrix; Generalize Inverse; Matrix Algebra (search for similar items in EconPapers)
Date: 1999
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:978-3-662-03976-2_2

Ordering information: This item can be ordered from
http://www.springer.com/9783662039762

DOI: 10.1007/978-3-662-03976-2_2

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 ().