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## AI-GEOSTATS: AI-geostats: Goodness-of-fit test / Kriging of multiple samples

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• Hello AI-Geostat members, I have two questions: 1 - test for goodness-of-fit for spatial estimate. I need to compare exactly two kinds of spatial estimates. At
Message 1 of 2 , Oct 9, 2001
Hello AI-Geostat members,
I have two questions:
1 - test for goodness-of-fit for spatial estimate.
I need to compare exactly two kinds of spatial
estimates. At least one of them has no defined shape
of probability distribution. If the data are not
spatial and weighted, goodness-of-fit of any
distribution and data could be evaluated vu e.g.
Kolmogorov-Smirnov test. However, this test does not
work with weighted data. Does anybody know any similar
or modified test for evaluation of goodness-of-fit of
point estimates with data?
2 - Kriging weights for repeated samples.
I test a set of samples. Some of them are nuggets.
These samples were repeated. Consequently, the
repeated samples have identical coordinates but
different value. Matrix of linear equations used for
calculation of Kriging weights thus contains pairs and
identical equations and has no solution. Calculation
of only one equation for each sample location
(regardless of number of samples at each location)
could be a solution, but it really is not kosher. It
could perhaps give acceptable results, but it is
insufficient for theoretical reasoning of mathematical
proofs. Could you advise a mathematically precise
solution of the problem?
Thanks for help.
With regards
Karel SEVCIK

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• If you have multipl esamples at certain locations, what you can do is to modify the diagonal entry in your kriging system. Instead of gamma(0)=0 put in
Message 2 of 2 , Oct 10, 2001
If you have multipl esamples at certain locations,
what you can do is to modify the diagonal entry in
your kriging system. Instead of gamma(0)=0 put in
gamma(0)=(n-1)/n times nugget effect. This tells the
kriging system you have replicates and it will adjust
weights and optimal estimator accordingly.

This is documented in Matheron's original works.

Isobel Clark
http://uk.geocities.com/drisobelclark

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