Index: The Book of Statistical Proofs ▷ Statistical Models ▷ Multivariate normal data ▷ Inverse general linear model ▷ Best linear unbiased estimator

Theorem: Let there be a general linear model of $Y \in \mathbb{R}^{n \times v}$

\[\label{eq:glm} Y = X B + E, \; E \sim \mathcal{MN}(0, V, \Sigma)\]

implying the inverse general linear model of $X \in \mathbb{R}^{n \times p}$

\[\label{eq:iglm} X = Y W + N, \; N \sim \mathcal{MN}(0, V, \Sigma_x) \; .\]

where

\[\label{eq:BW-Sx} B \, W = I_p \quad \text{and} \quad \Sigma_x = W^\mathrm{T} \Sigma W \; .\]

Then, the weighted least squares solution for $W$ is the best linear unbiased estimator of $W$.

Proof: The linear transformation theorem for the matrix-normal distribution states:

\[\label{eq:matn-ltt} X \sim \mathcal{MN}(M, U, V) \quad \Rightarrow \quad Y = AXB + C \sim \mathcal{MN}(AMB+C, AUA^\mathrm{T}, B^\mathrm{T}VB) \; .\]

The weighted least squares parameter estimates for \eqref{eq:iglm} are given by

\[\label{eq:iglm-wls} \hat{W} = (Y^\mathrm{T} V^{-1} Y)^{-1} Y^\mathrm{T} V^{-1} X \; .\]

The best linear unbiased estimator $\hat{\theta}$ of a certain quantity $\theta$ estimated from measured data $y$ is 1) an estimator resulting from a linear operation $f(y)$, 2) whose expected value is equal to $\theta$ and 3) which has, among those satisfying 1) and 2), the minimum variance.


1) First, $\hat{W}$ is a linear estimator, because it is of the form $\tilde{W} = M X$ where $M$ is an arbitrary $v \times n$ matrix.


2) Second, $\hat{W}$ is an unbiased estimator, if $\left\langle \hat{W} \right\rangle = W$. By applying \eqref{eq:matn-ltt} to \eqref{eq:iglm}, the distribution of $\tilde{W}$ is

\[\label{eq:W-hat-dist} \tilde{W} = M X \sim \mathcal{MN}(M Y W, M V M^T, \Sigma_x) \;\]

which requires that $M Y = I_v$. This is fulfilled by any matrix

\[\label{eq:M-D} M = (Y^\mathrm{T} V^{-1} Y)^{-1} Y^\mathrm{T} V^{-1} + D\]

where $D$ is a $v \times n$ matrix which satisfies $D Y = 0$.


3) Third, the best linear unbiased estimator is the one with minimum variance, i.e. the one that minimizes the expected Frobenius norm

\[\label{eq:Var-W} \mathrm{Var}\left( \tilde{W} \right) = \left\langle \mathrm{tr}\left[ (\tilde{W} - W)^\mathrm{T} (\tilde{W} - W) \right] \right\rangle \; .\]

Using the matrix-normal distribution of $\tilde{W}$ from \eqref{eq:W-hat-dist}

\[\label{eq:W-hat-W-dist} \left( \tilde{W} - W \right) \sim \mathcal{MN}(0, M V M^\mathrm{T}, \Sigma_x)\]

and the property of the Wishart distribution

\[\label{eq:E-XX} X \sim \mathcal{MN}(0, U, V) \quad \Rightarrow \quad \left\langle X X^\mathrm{T} \right\rangle = \mathrm{tr}(V) \, U \; ,\]

this variance can be evaluated as a function of $M$:

\[\label{eq:Var-M} \begin{split} \mathrm{Var}\left[ \tilde{W}(M) \right] &\overset{\eqref{eq:Var-W}}{=} \left\langle \mathrm{tr}\left[ (\tilde{W} - W)^\mathrm{T} (\tilde{W} - W) \right] \right\rangle \\ &= \left\langle \mathrm{tr}\left[ (\tilde{W} - W) (\tilde{W} - W)^\mathrm{T} \right] \right\rangle \\ &= \mathrm{tr}\left[ \left\langle (\tilde{W} - W) (\tilde{W} - W)^\mathrm{T} \right\rangle \right] \\ &\overset{\eqref{eq:E-XX}}{=} \mathrm{tr}\left[ \mathrm{tr}(\Sigma_x) \, M V M^\mathrm{T} \right] \\ &= \mathrm{tr}(\Sigma_x) \; \mathrm{tr}(M V M^\mathrm{T}) \; . \end{split}\]

As a function of $D$ and using $D Y = 0$, it becomes:

\[\label{eq:Var-D} \begin{split} \mathrm{Var}\left[ \tilde{W}(D) \right] &\overset{\eqref{eq:M-D}}{=} \mathrm{tr}(\Sigma_x) \; \mathrm{tr}\!\left[ \left( (Y^\mathrm{T} V^{-1} Y)^{-1} Y^\mathrm{T} V^{-1} + D \right) V \left( (Y^\mathrm{T} V^{-1} Y)^{-1} Y^\mathrm{T} V^{-1} + D \right)^\mathrm{T} \right] \\ &= \mathrm{tr}(\Sigma_x) \; \mathrm{tr}\!\left[ (Y^\mathrm{T} V^{-1} Y)^{-1} \, Y^\mathrm{T} V^{-1} V V^{-1} Y \; (Y^\mathrm{T} V^{-1} Y)^{-1} + \right. \\ &\hphantom{=\mathrm{tr}(\Sigma_x) \; \mathrm{tr}\!\left[\right.} \left. \, (Y^\mathrm{T} V^{-1} Y)^{-1} Y^\mathrm{T} V^{-1} V D^\mathrm{T} + D V V^{-1} Y (Y^\mathrm{T} V^{-1} Y)^{-1} + D V D^\mathrm{T} \right] \\ &= \mathrm{tr}(\Sigma_x) \left[ \mathrm{tr}\!\left( (Y^\mathrm{T} V^{-1} Y)^{-1} \right) + \mathrm{tr}\!\left( D V D^\mathrm{T} \right) \right] \; . \end{split}\]

Since $D V D^\mathrm{T}$ is a positive-semidefinite matrix, all its eigenvalues are non-negative. Because the trace of a square matrix is the sum of its eigenvalues, the mimimum variance is achieved by $D = 0$, thus producing $\hat{W}$ as in \eqref{eq:iglm-wls}.

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Metadata: ID: P268 | shortcut: iglm-blue | author: JoramSoch | date: 2021-10-21, 16:46.