Abstract
The standard linear model assumes the data vector has a covariance matrix of \(\sigma ^2 I\). Sections 2.7 and 3.8 extended the theory to having a covariance matrix of \(\sigma ^2 V\) where V was known and positive definite. This chapter extends the theory by allowing V to be merely nonnegative definite.
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Christensen, R. (2020). General Gauss–Markov Models. In: Plane Answers to Complex Questions. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-32097-3_10
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DOI: https://doi.org/10.1007/978-3-030-32097-3_10
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