Abstract
Recall from Chap. 7 that the least squares estimators of estimable functions are best linear unbiased estimators (BLUEs) of those functions under the Gauss–Markov model. But it turns out that this is not necessarily so under linear models having a more general variance–covariance structure, such as the Aitken model. In this chapter, we consider estimators that are best linear unbiased under the Aitken model . The first section considers the special case of an Aitken model in which the variance–covariance matrix is positive definite; BLUE in this case is also called generalized least squares estimation. The second section considers the general case. The third section characterizes those Aitken models for which the least squares estimators of estimable functions are BLUEs of those functions. A final section briefly considers an attempt to extend BLUE to the general mixed linear model.
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References
Christensen, R. (2011). Plane answers to complex questions (4th ed.). New York: Springer.
Rao, C. R. (1967). Least squares theory using an estimated dispersion matrix and its application to measurement of signals. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 355–372).
Zyskind, G. (1967). On canonical forms, nonnegative covariance matrices, and best and simple least squares estimators in linear models. Annals of Statistics, 38, 1092–1110.
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Zimmerman, D.L. (2020). Best Linear Unbiased Estimation for the Aitken Model. In: Linear Model Theory. Springer, Cham. https://doi.org/10.1007/978-3-030-52063-2_11
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DOI: https://doi.org/10.1007/978-3-030-52063-2_11
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