2318RE: [ai-geostats] Traditional OCK or Standardize OCK?
- Jan 5, 2006The downside of SOCK (often not mentioned) is that as a minimum requirement one must know the difference(s) between the population means (i.e., the means of the random functions) of the primary and secondary variables. In practice, one rarely knows these and uses the differences between the sample means instead, which is incorrect, unless one takes the associated estimation errors into account. However, when the BLUE of the differences between population means is used and the associated estimation errors are taken into account, then I suspect that SOCK boils down to something very close or identical to TOCK. Along similar lines, recall that substituting the BLUE of the population mean in the simple kriging equations yields a predictor that is identical to the ordinary kriging predictor (I think it is in Cressie's book, but in fact it is not that difficult to establish this result).
The main (only?) purpose of using ordinary kriging instead of simple kriging is that one often does not know the population mean and cannot simply assume that it is equal to the sample mean or some other combination of the sample data. That is why ordinary kriging is used much more often than simple kriging. It puzzles me why so many geostatisticians so easily replace TOCK by SOCK and ignore the problem above. It is not the right method to avoid large and many negative weights, there are much better ways for that (see discussion of one month ago).
Gerard B.M. Heuvelink
Soil Science Centre
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From: Pierre Goovaerts [mailto:Goovaerts@...]
Sent: donderdag 5 januari 2006 0:20
To: Adrián Martínez Vargas; Behrang Kushavand; ai-geostats@...
Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK?
The main difference between SOCK and TOCK is that, in the standardized
form, only one unbiasedness constraint is imposed, i.e. the sum of all
primary and secondary data weights is one, while in the traditional
version a separate constraint is applied for each variable, i.e.
sum of primary data weights is one and the sum of secondary data
weights is zero for each secondary variable. The traditional
constraints lead to larger and more frequent negative weights
for the secondary variables. The difference between SOCK and
TOCK estimates is expected to increase as differences between
the variance of primary and secondary variables increases.
The different types of cokriging are described and compared in the
Goovaerts, P. 1998. Ordinary cokriging revisited.
Mathematical Geology, 30(1): 21-42.
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From: Adrián Martínez Vargas [mailto:amvargas@...]
Sent: Wed 1/4/2006 12:53 PM
To: Behrang Kushavand; ai-geostats@...
Subject: Re: [ai-geostats] Traditional OCK or Standardize OCK?
In the definition of the cross variogram you can see that it is not
adimentional (depend of units >> Km, %, ppm, etc.), you can avoid this
effect using standardize Ordinary Co-Kriging.
From: "Behrang Kushavand" <Kushavand@...>
Date: Wed, 4 Jan 2006 19:55:01 +0330
Subject: [ai-geostats] Traditional OCK or Standardize OCK?
> Dear All,____________________________________________________________________________________________
> Is it true that estimation variance of standardize Ordinary Co-Kriging
> (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging
> What is the advantage of TOCK to SOCK (I think it is about negative
> weights) and are there any criteria to choice TOCK or SOCK?
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