Pierre Goovaerts writes:
Hello,
In fact, once the pseudo-sill A cancels out from
the system of linear equations, the system is
expressed in terms of semivariograms
I use to think in terms of covariances since
it's more intuitive, and the simple kriging
system can only be
In 1971, I and 16 other people were taught in a short
course at Fontainebleau given by Andre Journel and
Charles Huijbregts, completely in terms of the
semi-variogram with the covariance only being brought
in as a special case when you could ensure
stationarity of both mean and standard
It is well known that when inverting a
matrix it is much better (for numerical reasons)
that the higher values
are on the diagonal and the lower values far off the
diagonal.
Have you not heard of pivoting?
The computational problems of using a matrix based
on the semi-variogram rather
Excuse my persistence, but I think you are missing the
point here.
If you can produce a covariance function by
subtracting the semi-variogram from an arbitrary
constant AND if it makes no difference to the
resulting equations, you are simply constructing the
equations WITH the semi-variogram.
Dear Denis
I am sorry you think that I am being agressive. I
thought I was being quite reasonable, but perception
is a subjective thing. I think it is important for
readers of this list to understand that there are
different ways of coming to the same answer and that
there are different opinions
I'm hesitant to participate for fear of being jumped on, but
I think there is another aspect to the original question that
has not been addressed.
From a practical standpoint, we have complete discretion in choosing
a model. In theory, the properties of the sample do not inform the choice.
Hi Yetta
Jump in, the water is lovely! All contributions
equally valid in my e-mail box ;-)
I have to confess that I have rarely used an unbounded
semi-variogram model. In mining applications, in my
experience (which is limited to 30 years in economic
mineralisations) semi-variograms which
Dear Isobel:
In the case of intrinsic random functions the covariance (non ergodic covariance) and
the
correlogram are artifacts (see my example in ai-geostats).
I prefer to use the variogram. If you whish to have a symetric system, with no
problems of pivoting,
use the Matheron System, and
Thank you, Marco!
My point exactly.
Isobel
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Hi guys,
I promised myself I would not waste more time
on this futile discussion about covariance and variogram,
but it seems that the discussion has drifted far away from
the initial comment by Isobel or that most people don't
remember what was the initial question.
Isobel's comment originated
Hi all,
A few more references to the Covariance Vs. Semi-
variograme discussion:
To support Semi-variograme: Cressie N.A.C. (1993)
Statistics for spatial data. New York Wiley. ( Page 70-
73) I believe that the original discussion appears in:
Cressie A.C. Noel. and Grondona O. Martin
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