Abstract
The concepts of a random vector, the expected value of a random vector, and the covariance of a random vector are needed before covering generalized least squares. Recall that for random variables Y i and Y j , the covariance of Y i and Y j is Cov(Y i , Y j ) ≡ σ i, j = E[(Y i −E(Y i ))(Y j −E(Y j )] = E(Y i Y j ) −E(Y i )E(Y j )provided the second moments of Y i and Y j exist.
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Olive, D.J. (2017). WLS and Generalized Least Squares. In: Linear Regression. Springer, Cham. https://doi.org/10.1007/978-3-319-55252-1_4
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DOI: https://doi.org/10.1007/978-3-319-55252-1_4
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