Index: The Book of Statistical ProofsStatistical Models ▷ Multivariate normal data ▷ General linear model ▷ Weighted least squares

Theorem: Given a general linear model with correlated observations

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

the weighted least sqaures parameter estimates are given by

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

Proof: Let there be an $n \times n$ square matrix $W$, such that

\[\label{eq:W-def} W V W^\mathrm{T} = I_n \; .\]

Since $V$ is a covariance matrix and thus symmetric, $W$ is also symmetric and can be expressed as the matrix square root of the inverse of $V$:

\[\label{eq:W-V} W W = V^{-1} \quad \Leftrightarrow \quad W = V^{-1/2} \; .\]

Left-multiplying the linear regression equation \eqref{eq:GLM} with $W$, the linear transformation theorem implies that

\[\label{eq:GLM-W} WY = WXB + WE, \; WE \sim \mathcal{MN}(0, W V W^\mathrm{T}, \Sigma) \; .\]

Applying \eqref{eq:W-def}, we see that \eqref{eq:GLM-W} is actually a general linear model with independent observations

\[\label{eq:GLM-W-dev} \tilde{Y} = \tilde{X}B + \tilde{E}, \; \tilde{E} \sim \mathcal{N}(0, I_n, \Sigma)\]

where $\tilde{Y} = WY$, $\tilde{X} = WX$ and $\tilde{E} = WE$, such that we can apply the ordinary least squares solution giving

\[\label{eq:WLS-qed} \begin{split} \hat{B} &= (\tilde{X}^\mathrm{T} \tilde{X})^{-1} \tilde{X}^\mathrm{T} \tilde{Y} \\ &= \left( (WX)^\mathrm{T} WX \right)^{-1} (WX)^\mathrm{T} WY \\ &= \left( X^\mathrm{T} W^\mathrm{T} W X \right)^{-1} X^\mathrm{T} W^\mathrm{T} W Y \\ &= \left( X^\mathrm{T} W W X \right)^{-1} X^\mathrm{T} W W Y \\ &\overset{\eqref{eq:W-V}}{=} \left( X^\mathrm{T} V^{-1} X \right)^{-1} X^\mathrm{T} V^{-1} Y \end{split}\]

which corresponds to the weighted least squares solution \eqref{eq:WLS}.


Metadata: ID: P107 | shortcut: glm-wls | author: JoramSoch | date: 2020-05-19, 06:27.