Hi,

If there is a very high nugget effect i would expect that the predictions 
are very close to the mean of the data, with very little variation. In 
this case you would get a very high correlation (either close to 1 or -1 - 
depending on how you calculated the residuals). Did you check for local 
outliers??? If you have a high percentage of local outliers kriging is not 
a good choice - in my experience -  stationarity is usually violated, and 
the predictions are very poor indeed. Maybe you should investigate other 
methods of interpolations ..... one of my favorite is multiquadric radial 
basis function which in many cases can be compared with kriging, performs 
better when a high percentage of local outliers exist, and does not 
require stationarity.

Monica

====================================
Monica Palaseanu-Lovejoy, PhD
Jacobs Technology
US Geological Survey
Florida Integrated Science Center
600 4th Street South
St. Petersburg, FL 33701
Ph: 727-803-8747 x 3068
Fx: 727-803-2031
email: [EMAIL PROTECTED]
====================================



"Gregoire Dubois" <[EMAIL PROTECTED]> 
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01/30/2008 06:59 AM
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AI-GEOSTATS: Correlation between kriging residuals and input data






Dear list, 
Having fit a variogram to a dataset (indoor radon measurements) and 
applied cross-validations, I noticed the perfect negative correlation 
(-0.95) between my kriging residuals and my input data. 
This means that I am overestimating as much the low values as I am 
underestimating the high values, something I am expecting since the mean 
of the residuals  -> 0, a property of kriging. Fine so far.
What I am puzzled about is of the possible reasons of getting such a 
strong slope (close to -1) of the plot of my residuals against my input 
data? 
This, I understand, highlights that I am doing a systematic error 
somewhere which I want to avoid obviously. I thought I extracted properly 
the spatially correlated component of my dataset (the variogram of my 
residuals seems to show a pure nugget effect) but I still can't find any 
reasonable explanation for the systematic errors. 
Any hints? I must have missed something obvious here. 
Many thanks for any feedback. 
Best regards, 
Gregoire 

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