Goovaerts, regarding the factorial kriging you mentioned below...is it possible 
to filter the micro component (nugget effect) from our spatial model? Because 
the magnitude of nugget effect is related to the magnitude of variance error as 
well.
 
Thank you.

Regards,---------------
M. Nur Heriawan
Earth Resources Exploration Research Group
Faculty of Mining and Petroleum Engineering
Institut Teknologi Bandung (ITB)
Jl. Ganesha 10 Bandung 40132 INDONESIA
http://www.mining.itb.ac.id/heriawan 




________________________________
From: Pierre Goovaerts <goovae...@terraseer.com>
To: seba <sebastiano.trevis...@libero.it>
Cc: José M. Blanco Moreno <jmbla...@ub.edu>; ai-geostats@jrc.it
Sent: Tue, February 2, 2010 12:27:09 AM
Subject: Re: AI-GEOSTATS: moving averages and trend

well Factorial Kriging Analysis allows you to tailor the filtering weights
to the spatial patterns in your data. You can use the same filter size but
different kriging weights depending on whether you want to estimate
the local or regional scales of variability.

Pierre


2010/2/1 seba <sebastiano.trevis...@libero.it>

Hi José
>Thank you for the interesting references. I'm going to give a look!
>Bye
>Sebastiano 
>
>
>
>At 15.46 01/02/2010, José M. Blanco Moreno wrote:
>
>Hello again,
>>I am not a mathematician, so I never worried too much on the theoretical 
>>reasons. You may be able to find some discussion on this subject in Eubank, 
>>R.L. 1999. Nonparametric Regression and Spline Smoothing, 2a ed. M. Dekker, 
>>New York.
>>You may be also interested on searching information in and related to 
>>(perhaps citing) this work: Altman, N. 1990. Kernel smoothing of data with 
>>correlated errors. Journal of the American Statistical Association, 85: 
>>749-759.
>>
>>En/na seba ha escrit: 
>>
>>Hi José
>>>Thank you for your reply.
>>>Effectively I'm trying to figure out the theoretical reasons for their use.
>>>Bye
>>>Sebas


-- 
Pierre Goovaerts

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