|
Hi, Isobel,
thank you very much for your exahustive and deeply
useful explanations!
Yes! I have something like a sill, that, if I
understood correctly, should put me on the safe side choosing to use ordinary
cokriging.
About my first question: yes! I have repeated
measures of density in each sampling area, and this allows me to have confidence
intervals. My next question: these estimates don't share the same accuracy,
there are confidence intervals narrower or larger then others. So, what the
variance to account for in the semi-variogram? An average of my estimates
variance? The largest?
About my third question: I had no answers for it.
Maybe I was not enough fair in explaining the problem, or maybe there are not
solutions. To be sure I'll try to explain better:
my doubt: imagine I collect some estimates of a
variable, and I have un uncertainty about them, say a 95% confidence
interval of 20, and I know this uncertainty. Now I develop a kriging model to
predict my variable, and I cross-validate it. Well, even if I had a perfect
model, completely precise and accurate, I should have a MAE of 10, more or less.
Do someone think this is correct? And, given that I know the uncertainty about
my estimates, is there a way to 'correct' the MAE of my cross-validation
accounting for it?
Greetings to all list members.
X Isobel - I ignored the presence of the King in
Frascati (and the same everyone around me). Usually people from Roma goes to
Frascati just to spend the evening drinking. I'll spread the information and I
will look like an history expert, so thank you again!
Cheers!
Daniele
|
