Estimability :: SAS/STAT(R) 13.1 User's Guide

Estimability

Subsections:

Given a response or dependent variable $\mb {Y}$ , predictors or independent variables $\mb {X}$ , and a linear expectation model $\mr {E}[\mb {Y}]=\mb {X} \bbeta$ relating the two, a primary analytical goal is to estimate or test for the significance of certain linear combinations of the elements of $\bbeta$ . For least squares regression and analysis of variance, this is accomplished by computing linear combinations of the observed $\mb {Y}$ s. An unbiased linear estimate of a specific linear function of the individual $\beta$ s, say $\mb {L} \bbeta$ , is a linear combination of the $\mb {Y}$ s that has an expected value of $\mb {L} \bbeta$ . Hence, the following definition:

A linear combination of the parameters $\mb {L} \bbeta$ is estimable if and only if a linear combination of the $\mb {Y}$ s exists that has expected value $\mb {L} \bbeta$ .

Any linear combination of the $\mb {Y}$ s, for instance $\mb {KY}$ , will have expectation $\mr {E}[\mb {KY}]=\mb {KX} \bbeta$ . Thus, the expected value of any linear combination of the $\mb {Y}$ s is equal to that same linear combination of the rows of $\mb {X}$ multiplied by $\bbeta$ . Therefore,

$\mb {L} \bbeta$ is estimable if and only if there is a linear combination of the rows of $\mb {X}$ that is equal to $\mb {L}$ —that is, if and only if there is a $\mb {K}$ such that $\mb {L}=\mb {KX}$ .

Thus, the rows of $\mb {X}$ form a generating set from which any estimable $\mb {L}$ can be constructed. Since the row space of $\mb {X}$ is the same as the row space of $\mb {X’X}$ , the rows of $\mb {X’X}$ also form a generating set from which all estimable $\mb {L}$ s can be constructed. Similarly, the rows of $(\mb {X’X})^{-}\mb {X’X}$ also form a generating set for $\mb {L}$ .

Therefore, if $\mb {L}$ can be written as a linear combination of the rows of $\mb {X}$ , $\mb {X’X}$ , or $(\mb {X’X})^{-}\mb {X’X}$ , then $\mb {L} \bbeta$ is estimable.

In the context of least squares regression and analysis of variance, an estimable linear function $\mb {L}\bbeta$ can be estimated by $\mb {L}\widehat{\bbeta }$ , where $\widehat{\bbeta }=(\mb {X’X})^-\mb {X’Y}$ . From the general theory of linear models, the unbiased estimator $\mb {L}\widehat{\bbeta }$ is, in fact, the best linear unbiased estimator of $\mb {L}\bbeta$ , in the sense of having minimum variance as well as maximum likelihood when the residuals are normal. To test the hypothesis that $\mb {L}\bbeta =\mb {0}$ , compute the sum of squares

$\mr {SS}(H_0\colon ~ \mb {L} \bbeta =\mb {0})=(\mb {L}\widehat{\bbeta })’ (\mb {L} (\mb {X’X})^{-}\mb {L}’)^{-1}\mb {L}\widehat{\bbeta }$

and form an F test with the appropriate error term. Note that in contexts more general than least squares regression (for example, generalized and/or mixed linear models), linear hypotheses are often tested by analogous sums of squares of the estimated linear parameters $(\mb {L}\widehat{\bbeta })’(\mr {Var}[\mb {L}\widehat{\bbeta }])^{-1}\mb {L}\widehat{\bbeta }$ .