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A generalized least-squares model
Studia Geophysica et Geodaetica, 1970A interpolation method is given for the case in which a function consisting of a systematic and a random part is to be estimated from measurements affected by errors. This is a combined problem of parameter estimation, filtering and prediction, which has applications in different fields of geodesy and gravimetry.
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Second‐order inference for generalized least squares
Canadian Journal of Statistics, 1987AbstractConfidence regions for generalized least squares are commonly derived from a measure of departure calculated on the tangent plane at the MLE or on the tangent plane at the true value; the first gives approximate confidence regions, the second exact.
Fraser, D. A. S., Massam, H.
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Feasible Generalized Least Squares Estimation
1984There are instances in econometric modeling when an investigator is willing to specify the structure of the error variance-covariance matrix, Ω, of a generalized least squares model up to a few unknown parameters, say θl θ2,..., θp. This would occur, for example, when correlation in the errors of a time series regression model is suspected or when ...
Thomas B. Fomby +2 more
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Alternating Least Squares in Generalized Linear Models
Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences), 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Solving Generalized Least-Squares Problems with LSQR
SIAM Journal on Matrix Analysis and Applications, 1999An iterative method, referred as \(\text{LSQR}(A^{-1})\), for solving augmented linear systems in generalized least squares sense is given. It is shown that the new method is a natural extension of the LSQR algorithm. Numerical experiments comparing the \(\text{LSQR}(A^{-1})\) method with the preconditioned Krylov method are considered, too.
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WLS and Generalized Least Squares
2017The 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 ...
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A generalized least-squares approach regularized with graph embedding for dimensionality reduction
Pattern Recognition, 2020Xiang-Jun Shen +2 more
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