Gregoire
The correlation between actual value and error of estimation is always
present to some extent and is simply due to the estimation process. High values
will b eunderestimated from neighbouring samples. Low values will be
overestimated from neighbouring samples. The only way you can remove this is by
using a more complex estimator than a weighted average.
Have you plotted the actual value versus the estimates? This will tell you
whether you are getting any meaningful prediction or not. Generally, the
stronger the correlation here the less you'll get with the errors.
FYI: we use (actual - estimate) in our discussions. Not sure why, just a
personal preference.
Isobel
http://www.kriging.com
Monica Palaseanu-Lovejoy <[EMAIL PROTECTED]> wrote:
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]>
Sent by: [EMAIL PROTECTED] 01/30/2008 06:59 AM Please respond to
"Gregoire Dubois" <[EMAIL PROTECTED]>
To
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Subject
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
