[mailto:owner-ai-geost...@jrc.ec.europa.eu] On Behalf Of seba
Sent: 02 February 2010 08:39
To: Pierre Goovaerts
Cc: ai-geostats@jrc.it
Subject: Re: AI-GEOSTATS: moving averages and trend
Hi Pierre
I think that for my task factorial kriging is a little bit
too much sophisticated (nevertheless, is there any
...@jrc.ec.europa.eu
[mailto:owner-ai-geost...@jrc.ec.europa.eu] *On Behalf Of *seba
*Sent:* 02 February 2010 08:39
*To:* Pierre Goovaerts
*Cc:* ai-geostats@jrc.it
*Subject:* Re: AI-GEOSTATS: moving averages and trend
Hi Pierre
I think that for my task factorial kriging is a little bit
too much sophisticated
---
*From:* owner-ai-geost...@jrc.ec.europa.eu
[mailto:owner-ai-geost...@jrc.ec.europa.eu] *On Behalf Of *seba
*Sent:* 02 February 2010 08:39
*To:* Pierre Goovaerts
*Cc:* ai-geostats@jrc.it
*Subject:* Re: AI-GEOSTATS: moving averages and trend
Hi Pierre
I think that for my task factorial
Hello,
Factorial kriging is not very sophisticated, it's just a slight variant of
kriging
that requires the modification of just a few lines of codes.
Anyways, I just posted a program to perform factorial kriging analysis
in the download section of my website. I hope your grid is not too big,
Dear Seba,
As far as I know, the shape of the kernel is not that important. Any
kernel will yield approximately the same results as long as the scaling
parameters (bandwidth) are equivalent. There are some theoretical as
well as practical reasons for choosing finite (compact) kernels - the
Hi José
Thank you for your reply.
Effectively I'm trying to figure out the theoretical reasons for their use.
Bye
Sebas
At 12.30 01/02/2010, José M. Blanco Moreno wrote:
Dear Seba,
As far as I know, the shape of the kernel is not
that important. Any kernel will yield
approximately the same
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
*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
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
