Least Squares Estimation for the Gauss–Markov Model

Abstract

Now that we have determined which functions c ^Tβ in a linear model {y, Xβ} can be estimated unbiasedly, we can consider how we might actually estimate them. This chapter presents the estimation method known as least squares. Least squares estimation involves finding a value of β that minimizes the distance between y and Xβ, as measured by the squared length of the vector y −Xβ. Although this seems like it should lead to reasonable estimators of the elements of Xβ, it is not obvious that it will lead to estimators of all estimable functions c ^Tβ that are optimal in any sense. It is shown in this chapter that the least squares estimator of any estimable function c ^Tβ associated with the model {y, Xβ} is linear and unbiased under that model, and that it is, in fact, the “best” (minimum variance) linear unbiased estimator of c ^Tβ under the Gauss–Markov model {y, Xβ, σ ²I}. It is also shown that if the mean structure of the model is reparameterized, the least squares estimators of estimable functions are materially unaffected.