Hi Perry, 

I am curious to see how others will reply to your second question on the
difference between a dataset with a trend and one that is
non-stationary! My reply may sound provocative: you can always remove a
trend when you recognize that there is one. Moving from non-stationarity
to stationarity, on the other hand, can be infinitely more complex (e.g.
moving to non-Euclidean space) 

:)


For what concerns the detection of trends, have a look at the variogram:
a quadratic/exp. increase usually means that there is a trend. Get rid
of the presumed trend and check the variogram of the residuals which
should clearly show a change of structure (if you had a trend
obviously). Quicker might be to use a moving windows strategy to plot
local averages and check if you see any structure (be careful that the
"structure" is not simply an anisotropy of your variable). You could
have a look into the old archives of AI-GEOSTATS. There have been very
nice replies from Donald Myers (see his publications) in the past on
these issues. see http://groups.yahoo.com/group/ai-geostats/ 

Regards

GD



-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 
Sent: 07 July 2005 03:49
To: [email protected]
Subject: RE: [ai-geostats] modelling trend and kriging type


Hi all 
I may know this already, but what are the symptoms of data with a trend?
What is the difference between a dataset with a trend and a
non-stationary dataset?
Cheers 


Perry Collier 
Senior Mine Geologist 
Ernest Henry Mine   
Xstrata Copper Australia 
Ph (07) 4769 4527 
Fx (07) 4769 4555 
E-mail [EMAIL PROTECTED] 
Web http://www.xstrata.com 
  
PO Box 527 
Cloncurry QLD 4824 
Australia 
  
"Light travels faster than sound. That is why some people appear bright 
until you hear them speak" 




-----Original Message----- 
From: Pierre Goovaerts [mailto:[EMAIL PROTECTED] 
Sent: Friday, 1 July 2005 12:54 AM 
To: Recep kantarci; [email protected] 
Subject: RE: [ai-geostats] modelling trend and kriging type 


To add to the excellent comments by Edzer and Gregoire, 
  
1. Universal kriging = kriging with a trend. The second terminology has
been proposed by Andre 
Journel who felt that the term "universal" was vague and misleadingly
"ambitious". 
  
2. Kriging with an external drift (KED) is mathematically the same as
universal kriging (UK). Secondary variables 
are simply replacing the spatial coordinates used in UK. 
  
3. Regression kriging denotes all the techniques where the trend is
modeled outside the kriging algorithm. 
There are various methods that can be used to model that trend, ranging
from linear regression 
to neural networks. Kriging is used to interpolate the residuals. In
practice these techniques have more 
flexibility than universal kriging in term of modeling the trend:
multiple variables either categorical or 
continuous can be incorporated  easily and many sofwtare are available
for this trend modeling. 
The only limitation is that the trend is modeled globally (i.e. the
regression coefficients are constant 
in space) while in KED the coefficients are reestimated within each
search window. 
  
Cheers, 
  
Pierre 
  
Pierre Goovaerts 
Chief Scientist at Biomedware 
516 North State Street 
Ann Arbor, MI 48104 
Voice: (734) 913-1098 
Fax: (734) 913-2201 
http://home.comcast.net/~goovaerts/ 
        -----Original Message----- 
        From: Recep kantarci [mailto:[EMAIL PROTECTED] 
        Sent: Thu 6/30/2005 9:38 AM 
        To: [email protected] 
        Cc: 
        Subject: [ai-geostats] modelling trend and kriging type 
        
        
        Dear ai-geostats members 
         
        When the data used has a trend, it is needed to model trend and
in this case there exists various types of kriging to apply (universal
kriging, kriging with a trend, regression kriging etc).
        If this is the case, does one should use the same type of
kriging or different depending on modeling the trend using coordinates
of target variable or using other (namely, secondary or auxillary)
variables such as elevation or topography ? That is , are there a
dinstinction depending on the type of variables to model the trend while
kriging?
         
        Best regards 
        Recep 
        
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