Thanks everybody for your comments, very helpful and interesting discussion.
As some of you propose, maybe a good first step would be a cross-validation
exercise to test the effects of these parameters,
I am going to take a look at the literature,
Thanks again, and have a nice day,
Sincerely,
Alí Santacruz
From: "M. Nur Heriawan" <[EMAIL PROTECTED]>
To: "Alí" Santacruz <[EMAIL PROTECTED]>, [email protected]
Subject: Re: AI-GEOSTATS: newbie question
Date: Mon, 3 Jul 2006 04:50:34 -0700 (PDT)
Dear Ali,
Regarding choosing the optimum kriging neighborhood,
an article related to this is:
J. Rivoirard, "Two key parameters when choosing the
kriging neighborhood", Mathematical Geology, Vol.19
(8), 1987, p. 851-856.
In this article mentioned that in the stationary case,
two parameters are especially interesting when
choosing the kriging neighborhood: (1) weight of the
mean, which shows how kriging depends on the
neighborhood, and (2) slope of the regression, which
indicates if the neighborhood is large enough.
By the way, in case you have a pure nugget effect in
your semi-variogram model, you better use all data
when performing kriging estimation.
Hope this helps.
Regards,
Nur Heriawan
------------
Department of Mining Engineering
Institut Teknologi Bandung (ITB)
Jl. Ganesha 10 Bandung 40132, Indonesia
http://www.isme-ix.org/
--- Alí Santacruz <[EMAIL PROTECTED]> wrote:
> Dear list members,
>
> I have a very simple question (I think):
>
> When I want to perform a kriging, I must define the
> number of nearest
> observations that should be used for the kriging
> prediction, or a maximum
> distance from the prediction location.
>
> What criteria should I use to set these parameters?
> Which is the optimum
> number of nearest neighbors?
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