PeijunI presume by the "pseudo" cross semi-variogram, you mean the 'non co-located' cross semi-variogram as opposed to the more traditional co-located cross semi-variogram?If so, the difference between the sill of your model and the nugget effect at zero is simply the classical covariance
DigbyThe variance of the residuals (whether regression or kriging) is the sum of the squared residuals divided by the degrees of freedom. Since the "degrees of freedom" is a fixed number, minimising the variance is identical to minimising the sum of squared residuals.IsobelDigby Millikan
PierreIf the relationship between your two variables is negative, the "pseudo" cross semi-variogram will start high and drop off, just like the co-located one. Difference is, the former doesn't go negative, the latter starts at zero and is all negative.One other feature of the "pseudo"
Dear Dr. Clark,
Thank you for reply.
You know that any point (i.e.
pixel) in an image has a value (graylevel value), which is different from sparsely
sampling data in geosciences.
We use the pseudo cross
variogram to characterize the spatial cross correlation between two variables.
PeijunThat is interesting to hear. I wish you luck in its use. If you are writing any reports, you may wish to refer to our original paper "A novel approach to co-kriging" published in the 1980s and downloadable from my personal site at http://uk.geocities.com/drisobelclark/resume (follow
Is there any intuitive meaning that the
mean square of the differences is
equal to the classical formulae for
variance? The variance can be written
in two different forms;
1. the variogram form.
2. the classical statistics variance form.
What are the properties, reasons for
