If I consider universal kriging (or multiple spatial regression) in matrix form as:
${\bf{V = XA + R }}$
where $\bf{R}$ is the residual and $\bf{A}$ are the trend coefficients, then the estimate of ${\bf{\hat A}}$ is:
${\bf{\hat A}}=(\bf{X^{T}C^{-1}X)^{-1}X^{T}C^{-1}V}$
(as I understand it), where $\bf{C}$ is the covariance matrix, if it is known. Then, the variance of the coefficients is:
$\text{VAR}({\bf{\hat A}})=(\bf{X^{T}C^{-1}X)^{-1}}$???
I am getting this from here.
How does one get from the estimate of ${\bf{\hat A}}$, to its variance? i.e. how can I derive that variance?