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General Gauss–Markov Models

  • Ronald Christensen
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

A general Gauss–Markov model is a model \({\rm Y}={\rm X}\beta + e,\quad {\rm E}(e) = 0,\quad {{\rm Cov}(e)}= \sigma^{2}{\rm V},\) where V is a known matrix. Linear models can be divided into four categories depending on the assumptions made about V: (a) V is an identity matrix, (b) V is nonsingular, (c) V is possibly singular but C(X) ⊂ C(V), (d) V is possibly singular.

Keywords

Markov Model Unbiased Estimate Full Rank Full Column Rank Estimable Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Mathematics and Statistics MSC01 11151 University of New MexicoAlbuquerqueUSA

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