Abstract
Let X be an n×u matrix of given coefficients with full column rank, i.e. rankX = u, β a u × 1 random vector of unknown parameters, y an n × 1 random vector of observations, D(y|σ2) = σ2P-1 the n× n covariance matrix of y, σ2 the unknown random variable which is called variance factor or variance of unit weight and P the known positive definite weight matrix of the observations. Then \( X\beta = E(y|\beta ){\mathbf{ }}with{\mathbf{ }}D(y|\sigma ^2 ) = \sigma ^2 P^{ - 1} {\mathbf{ }} \) is called a linear model.
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© 2007 Springer-Verlag Berlin Heidelberg
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(2007). Linear Model. In: Introduction to Bayesian Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72726-2_4
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DOI: https://doi.org/10.1007/978-3-540-72726-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72723-1
Online ISBN: 978-3-540-72726-2
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