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Estimation

  • Ronald Christensen
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

In this chapter, properties of least squares estimates are examined for the model
$$ Y = X \beta + e,\quad {\rm E}(e) = 0, \quad{\rm Cov}(e)=\sigma^{2}I. $$
The chapter begins with a discussion of the concepts of identifiability and estimability in linear models. Section 2 characterizes least squares estimates. Sections 3, 4, and 5 establish that least squares estimates are best linear unbiased estimates, maximum likelihood estimates, and minimum variance unbiased estimates.

Keywords

Mean Square Error Bayesian Analysis Unbiased Estimate Prediction Interval Bayesian Estimation 
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, LLC 2011

Authors and Affiliations

  1. 1.Department of Mathematics and Statistics MSC01 11151 University of New MexicoAlbuquerqueUSA

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