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

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  • Karel Sevcik
    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
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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 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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    • Isobel Clark
      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
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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
        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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