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.
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© 2011 Springer Science+Business Media, LLC
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Christensen, R. (2011). General Gauss–Markov Models. In: Plane Answers to Complex Questions. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9816-3_10
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DOI: https://doi.org/10.1007/978-1-4419-9816-3_10
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