# Best linear unbiased estimator

BLUE

Let

$$\tag{a1 } Y = X \beta + \epsilon$$

be a linear regression model, where $Y$ is a random column vector of $n$" measurements" , $X \in \mathbf R ^ {n \times p }$ is a known non-random "plan" matrix, $\beta \in \mathbf R ^ {p \times1 }$ is an unknown vector of the parameters, and $\epsilon$ is a random "error" , or "noise" , vector with mean ${\mathsf E} \epsilon =0$ and a possibly unknown non-singular covariance matrix $V = { \mathop{\rm Var} } ( \epsilon )$. A model with linear restrictions on $\beta$ can be obviously reduced to (a1). Without loss of generality, ${ \mathop{\rm rank} } ( X ) = p$.

Let $K \in \mathbf R ^ {k \times p }$; a linear unbiased estimator (LUE) of $K \beta$ is a statistical estimator of the form $MY$ for some non-random matrix $M \in \mathbf R ^ {k \times n }$ such that ${\mathsf E} MY = K \beta$ for all $\beta \in \mathbf R ^ {p \times1 }$, i.e., $MX = K$. A linear unbiased estimator $M _ {*} Y$ of $K \beta$ is called a best linear unbiased estimator (BLUE) of $K \beta$ if ${ \mathop{\rm Var} } ( M _ {*} Y ) \leq { \mathop{\rm Var} } ( MY )$ for all linear unbiased estimators $MY$ of $K \beta$, i.e., if ${ \mathop{\rm Var} } ( aM _ {*} Y ) \leq { \mathop{\rm Var} } ( aMY )$ for all linear unbiased estimators $MY$ of $K \beta$ and all $a \in \mathbf R ^ {1 \times k }$.

Since it is assumed that ${ \mathop{\rm rank} } ( X ) = p$, there exists a unique best linear unbiased estimator of $K \beta$ for any $K$. It is then given by the formula $K {\widehat \beta }$, where ${\widehat \beta } = { {\beta _ {V} } hat } = ( X ^ {T} V ^ {-1 } X ) ^ {-1 } X ^ {T} V ^ {-1 } Y$, which coincides by the Gauss–Markov theorem (cf. Least squares, method of) with the least square estimator of $\beta$, defined as ${ \mathop{\rm arg} } { \mathop{\rm min} } _ \beta ( Y - X \beta ) ^ {T} V ^ {- 1 } ( Y - X \beta )$; as usual, ${} ^ {T}$ stands for transposition.

Because $V = { \mathop{\rm Var} } ( \epsilon )$ is normally not known, Yu.A. Rozanov [a2] has suggested to use a "pseudo-best" estimator ${ {\beta _ {W} } hat }$ in place of ${ {\beta _ {V} } hat }$, with an appropriately chosen $W$. This idea has been further developed by A.M. Samarov [a3] and I.F. Pinelis [a4]. In particular, Pinelis has obtained duality theorems for the minimax risk and equations for the minimax solutions $V$ assumed to belong to an arbitrary known convex set ${\mathcal V}$ of positive-definite $( n \times n )$- matrices with respect to the general quadratic risk function of the form

$$R ( V,W ) = {\mathsf E} _ {V} ( {\widehat \beta } _ {W} - \beta ) ^ {T} S ( {\widehat \beta } _ {W} - \beta ) ,$$

$$V \in {\mathcal V}, W \in {\mathcal V},$$

where $S$ is any non-negative-definite $( p \times p )$- matrix and ${\mathsf E} _ {V}$ stands for the expectation assuming ${ \mathop{\rm Var} } ( \epsilon ) = V$. Asymptotic versions of these results have also been given by Pinelis for the case when the "noise" is a second-order stationary stochastic process with an unknown spectral density belonging to an arbitrary, but known, convex class of spectral densities and by Samarov in the case of contamination classes.

#### References

 [a1] C.R. Rao, "Linear statistical inference and its applications" , Wiley (1965) [a2] Yu.A. Rozanov, "On a new class of estimates" , Multivariate Analysis , 2 , Acad. Press (1969) pp. 437–441 [a3] A.M. Samarov, "Robust spectral regression" Ann. Math. Stat. , 15 (1987) pp. 99–111 [a4] I.F. Pinelis, "On the minimax estimation of regression" Th. Probab. Appl. , 35 (1990) pp. 500–512
How to Cite This Entry:
Best linear unbiased estimator. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Best_linear_unbiased_estimator&oldid=46043
This article was adapted from an original article by I. Pinelis (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article