Dear Waghei,
At first I would not trust only in the traditional semivariogram. You
should better use more robust measures to evaluate the semivariogram
structures. There are several possibilities like correlogram or other
standardized variogram estimators (Srivastava, Parker 1989 - see below for
full reference). They are robust against outliers/extreme values, positive
skewness. Did you test for a trend?
In addition, did you try to vary the lag distance? Perhaps you will get a
better spatial continuity if you increase/decrease it.
If there should be a periodic structure you could use for variogram
modelling the periodical or hole effect model. But first you should think
of the underlying physical processes.
Hope this helps, Ulrich
References:
Srivastava, R.M.; Parker, H.M. 1989: Robust measures of spatial
continuity. In: Armstrong, M.1989 (Ed.): Geostatistics. Vol.1, Dordrecht,
p. 295-308.
On 2001.05.06 12:07:01 +0200 Yadollah (Majid) Waghei wrote:
> Hello list members
> My Empirical Variogram for a spatial data set has an cubic like form(see
> below). Then the ordinary variogram models such as Exponential,
> Gaussian, Linear, Spherical models is not best fit to my data( Because
> these models are monotone (increasing) functions). Then I need to a
> suitabe variogram model for such Data.
>
> A graph of semivariogram
> gamma(h)
> |.
> | .
> | . .
> |..
> | .. .
> |. . .
> | . ..
> | .
> | .
> |. . . .
> | . . .
> |_ h
> Thank you
> Waghei
> Dep.of Biostatistics
> Tarbiat Modarres Univ.(Tehran)Po.Box: 14115-111
> Tel:8011001-3872 Fax:8007989
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