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Part of the book series: Lecture Notes in Statistics ((LNS,volume 39))

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Abstract

With the linearization of the basic model and its covariance matrix at hand we now start on the estimation of the components. We’ll do this by calculating the ordinary least squares estimates for our linear model, and by discussing what is meant by “estimable function” in our context. As will be shown the problem is that the covariance we’ve found is singular. This, along with the possibly sticky point that the reader may have already noticed: the covariance is a function of the parameters (components) we wish to estimate. These issues are sorted out here and the the ordinary least squares estimates are related to other variance component estimation methods.

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© 1986 Springer-Verlag Berlin Heidelberg

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Malley, J.D. (1986). The Ordinary Least Squares Estimates. In: Optimal Unbiased Estimation of Variance Components. Lecture Notes in Statistics, vol 39. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7554-2_4

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  • DOI: https://doi.org/10.1007/978-1-4615-7554-2_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-96449-2

  • Online ISBN: 978-1-4615-7554-2

  • eBook Packages: Springer Book Archive

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