Title: RE: [ai-geostats] Sill versus least-squares classical variance estimate
Meng-Ying
samples taken beyond the range are, in fact, far enough apart from one another! The sill is - to all intents and puposes - equal to the variance of the data (This fails if there are trends in the data
Meng-Ying
We are talking about estimating the variance of a set
of samples where spatial dependence exists.
The classical statistical unbiassed estimator of the
population variance is s-squared which is the sum of
the squared deviations from the mean divided by the
relevant degrees of freedom.
Dear List,
I think I'd like to state my problem more clearly.
What I think to be the estimate of the overall variance is the expected
variance in the future samples. This have to do with what kind of sampling
scheme we use in the future, however.
If we could assume the future samples to be
