849Re: GEOSTATS: Nugget Effect
- Jun 1, 1998Bill Thayer wrote:
>My latest request for input concerns the calculation of the value of the(a) For highly skewed data, variograms become extremely sensitive
>variogram at h = 0. I have a highly skewed data set with very high
>short scale variability. This has caused problems with obtaining a good
>estimate of the nugget effect, primarily due to 2-3 high values. The
>data set I am working with contains the analytical results for 25
>duplicate samples. (I have used the highest of the dupe data in the
>variogram calculations). I have used this data (in a spreadsheet) to
>estimate the variogram value at h = 0. I would like to receive feedback
>on this approach - Is it a valid approach? What other approaches can I
to those high (possibly outlier?) values. Variability might be dependent
on location as well (proportional effect). Consider: (1) applying a
to the data to smooth out high values, e.g. lognormal, but be wary of
the pros and cons of simply applying a backtransform on kriged or simulated
values to get the original raw data (there were some exchanges on this
subject a couple of weeks ago that you might want to skim through in
the ai-geostats archives); or (2) applying some sort of "robust"
e.g. the family of general or pairwise relative variograms or nonergodic
covariance measures to filter out the spatial dependence of variability.
(b) Out of curiousity, why use the highest value among the duplicates?
Why not the mean? Or the mode? Or the median?
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