WLS and Generalized Least Squares

David J. Olive
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David J. Olive: Southern Illinois University, Department of Mathematics

Chapter Chapter 4 in Linear Regression, 2017, pp 163-173 from Springer

Abstract: 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.

Keywords: Ordinary Little Square; Random Vector; Ordinary Little Square Regression; Residual Plot; Cholesky Decomposition (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-55252-1_4

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DOI: 10.1007/978-3-319-55252-1_4

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