Abstract
Chapters 2 and 3 deal with alternatives to the ordinary least squares estimator \( \hat \beta \) for ß. Any of these can be viewed as being based on some linear estimator for ß , supposed to be admissible within the set of all linear estimators. This guarantees that the considered alternative linear estimator is better than the ordinary least squares estimator for at least one possible value of the unknown ß. The actual chapter investigates the structure of linearly admissible estimators.
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© 2003 Springer-Verlag Berlin Heidelberg
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Groß, J. (2003). Linear Admissibility. In: Linear Regression. Lecture Notes in Statistics, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55864-1_4
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DOI: https://doi.org/10.1007/978-3-642-55864-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40178-0
Online ISBN: 978-3-642-55864-1
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