Abstract
In this chapter some linear and matrix methods are introduced that will simplify later work, particularly the linearization of the basic reduced model, a subject we take up in the next chapter. The notions of vec, mat(m,n), I(m,n), tensor products ⊗, and some relations between them are discussed. Next are the space of symmetric matrices, its natural inner product, and a useful projection lemma.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1986 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Malley, J.D. (1986). Basic Linear Technique. In: Optimal Unbiased Estimation of Variance Components. Lecture Notes in Statistics, vol 39. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7554-2_2
Download citation
DOI: https://doi.org/10.1007/978-1-4615-7554-2_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-96449-2
Online ISBN: 978-1-4615-7554-2
eBook Packages: Springer Book Archive