When comparing kriging versus regression, I meant
using linear regression between sparse and exhaustive
datasets to interpolate the sparse one, since as Digbi
Milligan pointed out in general case regression is not
an estimation method.
--- Gali Sirkis [EMAIL PROTECTED] wrote:
Seumas,
see
Hi, members,
I am estimating nitrogen mineralisation rate on a 25 X 25 ha field. One of
my efforts is to find out optimum sampling density to examine nitrogen
mineralisation in my plot. How can I exploit a semivariogram to answer this
question? Does anyone have suggestions and also some relevant
Hello all. I'm performing unconditional sequential indicator simulation over a
3D domain. As the method requires, I have specified the data cdf at (10)
thresholds, and have also defined parameters for an (exponential) variogram at
each threshold. When I run the simulation algorithm using
Hi Eric,
I am guessing that the sampling density in 3D is small, hence
at the beginning of the simulation procedure when the grid is
essentially empty the estimate depends mainly on the type of trend
model you adopt, i.e. a global mean for SK or a locally re-estimated
local mean for OK. It looks
Hi all,
picking up on Jeff's point about collocated cokriging: what is the
difference between this technique (which I'm not familiar with) and an
autoregressive regression models such as CAR, SAR etc?
Thanks
Volker
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Volker Bahn
Dept. of Wildlife Ecology -
