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Linear Models pp 111-154 | Cite as

Exact and Stochastic Linear Restrictions

  • Calyampudi Radhakrishna Rao
  • Helge Toutenburg
Part of the Springer Series in Statistics book series (SSS)

Abstract

As a starting point, which was also the basis of the standard regression procedures described in the previous chapters, we take T i.i.d. samples of the variables y and X 1,..., X K . If the classical linear regression model y = Xβ+∊ with its assumptions may be assumed to be a realistic picture of the underlying relationship, then the least-squares estimator b = (XX)−1 Xy 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 Mixed Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Calyampudi Radhakrishna Rao
    • 1
  • Helge Toutenburg
    • 2
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Institut für StatistikUniversität MünchenMünchenGermany

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