• Ronald ChristensenEmail author
Part of the Springer Texts in Statistics book series (STS)


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.


Mean Square Error Bayesian Analysis Unbiased Estimate Prediction Interval Bayesian Estimation 
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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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