Abstract
As a starting point, which was also the basis of the standard regression procedures described in the previous chapters, we take a T -dimensional sample of the variables y and X1, . . ., XK. If the classical linear regression model y = Xβ + ε with its assumptions is assumed to be a realistic picture of the underlying relationship, then the least-squares estimator b = (X′X)−1X′y is optimal in the sense that it has smallest variability in the class of linear unbiased estimators for β.
Keywords
- Unbiased Estimator
- Auxiliary Information
- Linear Restriction
- Dispersion Matrix
- Good Linear Unbiased Estimator
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© 2008 Springer-Verlag Berlin Heidelberg
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(2008). Exact and Stochastic Linear Restrictions. In: Linear Models and Generalizations. Springer Series in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74227-2_5
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DOI: https://doi.org/10.1007/978-3-540-74227-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74226-5
Online ISBN: 978-3-540-74227-2
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