Donald,
Thanks for your reply,
my comments;
Digby
I don't understand some of your comments:
1. Stationarity is not a property of data, it is a property of the
underlying random function (no matter which form of stationarity you are
considering).
+ +
Dear List members,
I am looking for a reference on interpretation of the Kriging error versus the
sample variance. Am I correct in assumung that in any kriged interpolation
where the Kriging error is greater than the sample varience then the sample
mean would be a better estimate at that
Russell
Absolutely on the spot.
We call this the 'ygiagam' criterion (your guess is as
good as mine) ;-)
Isobel Clark
http://geoecosse.bizland.com/news.html
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Hi Russell,
I am assuming you refer to kriging error variance.
If your semivariogram is bounded and has a sill
close to the sample variance, then the simple kriging
estimate will automatically be the global mean
when the kriging variance is the sample variance
(that is when all observations are
You may have to consider the stationarity of your data, i.e. theoretically
your sample mean is better than your kriged estimate if your kriginng error
is greater than your kriging variance, but you did make the assumption that
your data is stationary when you kriged it, i.e. constant mean and
Russell,
If you have time to get to your library there is a book
Geostatistical Ore Reserve Estimation 1977 M.David
although related to geostatistics for mining this book
is written by a renowned geostatistician and has an
excellent diagrammatic representation of trend, drift
and
