GEOSTATS: cdf and ccdf
- Dear all,
I have some questions on the difference between the cumulative
distribution function (cdf) and the conditional cumulative
distribution function (ccdf).
I am not sure when I do infer the cdf and when do I infer the
ccdf by using various geostatistical tools.
If I want to infer the cdf of my continous variable Z at location u
(Z(u)) I have to calculate the sample histogram of all samples z(u_n,
n=1,...,n). Then I can determine the cdf-mean (expected value) which
is the sample mean (possibly the declustered mean)? And the a priori
covariance/variance is my cdf-covariance/variance?
When I use kriging I get the ccdf-mean (?) as an least squared error
estimate which is my kriging estimate z*(u) (using the neighbouring
sample values z(u_n)). The ccdf-covariance (?) is inferred through
the covariance/variogram model that is specified before.
In contrast, indicator kriging provides the whole ccdf (?) at an
unsampled location u inferred through the kriging of the transformed
sample values around.
So in both approaches
1) I use the neighbouring data (to infer the ccdf)?
2) in the first case I only infer the first two moments of the
ccdf (mean, covariance), in the indicator approach the whole ccdf is
I would be grateful for any comments to see if I am on the right or
on the wrong way.
Thanks so far.
Department of Soil Science
The University of Trier
address: Engelstr.104, 54292 Trier, Germany
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