Summary
A characterization of best linear unbiased estimators is given in the case of the general linear model. In addition necessary and sufficient conditions are derived for a given estimable function to have a best linear unbiased estimator. In particular models for which each estimable function has a best linear unbiased estimator are characterized. The conditions stated are given in a computational atractive form. The problems are discussed from the coordinate-free point of view. This is important for the results can be easily adopted in the case of estimation of variance components. The problem of estimation of either treatment or block effects in a mixed model serves as an example which illustrates the applicability of the results.
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Zmyślony, R. (1980). A Characterization of Best Linear Unbiased Estimators in the General Linear Model. In: Klonecki, W., Kozek, A., Rosiński, J. (eds) Mathematical Statistics and Probability Theory. Lecture Notes in Statistics, vol 2. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7397-5_27
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DOI: https://doi.org/10.1007/978-1-4615-7397-5_27
Publisher Name: Springer, New York, NY
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