Re: AI-GEOSTATS: Outliers problems
It is always a problem how to handle outliers properly. Generally speaking,
it depends on your purpose. The following are just some tips based on my
If you would like to estimate the representative mean value, a median, or
percentiles, or the mean value based on some kind of data transformation
(towards normality) will be good enough. You may also be aware that the
samples should be spatially declustered, and they belong to one population.
You will over-estimate the mean value if the samples are spatially clustered
in the high value areas.
If you want to estimate the robust variogram, outliers may be removed in the
variogram calculation, and be put back in kriging. Using median or other
methods to estimate the variogram may be considered, but I haven't found it
Outliers are also related to probability distribution. Those which are
outliers in the raw data may behave well in the transformed data set.
Backtransformation (for positively skewed data) in kriging may be
problematic as the estimated values may be lower than they should be in the
original scale if they were backtransformed directly.
Hope you find these comments helpful.
Dr. Chaosheng Zhang
Lecturer in GIS
Department of Geography
National University of Ireland
Tel: +353-91-524411 ext. 2375
----- Original Message -----
From: "ali qadrouci" <qadrouci_ali@...>
Sent: Wednesday, January 29, 2003 9:31 AM
Subject: AI-GEOSTATS: Outliers problems
> Dear listers,
> I have to apply a cutting on a set of grades coming from underground
> development or drill holes to eliminate the high values (outliers). As you
> know these values overestimate the average value and we have a great
> difference between the grade plant process and the estimated geological
> Can someone help me for this and give me a method to apply to eliminate
> these high values.
> thanks for any advice, Ali
> MSN Messenger : discutez en direct avec vos amis !
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