Oh, I'm not sure, but I have the feeling that, in this discussion, we
are mixing the non-conditional distribution (a.k.a. the global
distribution) with the conditional (local) one... and by the way,
dismissing confidence intervals using simple kriging variance for
gaussian distributions is equivalent to dismissing *prediction*
intervals (NOT *estimation* intervals of the mean) for a standard
regression Y=a+bX... the length of both do not depend on the "data
value" (the X of a regression, the observed values in SK), but I hope
that noone will doubt of the fitness of confidence intervals for a
simple regression...
Regards
Raimon
En/na tom andrews ha escrit:
Dear List
For me kriging variance may convey distribution of random variable
(the same for each random variable).
Ok, I'm not going to argue what kriging variance really is.
I can imagine mean (central value) of gaussian distribution +/- sigma.
I can imagine that sigma can be multiplied by quantile of standard
gaussian
distribution, I see in my mind how confidence intervals are growing up to
e.g. 95% of total area under the curve.
Next I see "poor" spread value in the tail of gaussian distribution
with some error (e.g. error of estimation) equal to e.g. 0,1 sigma.
But I can not imagine how these error intervals somewhere in the tail of
gaussian distribution and equal to +/- 0,1 sigma grow up to 95% of total
area under the curve when multiplied by 1,96.
Could somebody help me?
Best Regards
tom
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