Dear all,
Happy New Year!
Can we regard n=1 in the case of point kriging?
I think there is a trap in the question: How good is estimation using
kriging system that uses a nugget model?
My understanding is that when the spatial autocorrelation of a variable
behaves like a nugget effect, any spatial estimation is not allowed, no
matter how good it declares. What you can do is only to describe it
using conventional statistics, and perhaps explain the results on a
point basis: e.g., on a symbol map, not a contour or distribution map.
Cheers,
Chaosheng
Dr. Chaosheng Zhang
Lecturer in GIS
Department of Geography
National University of Ireland, Galway
IRELAND
Tel: +353-91- 49 2375
Fax: +353-91- 49 5505
Email: [EMAIL PROTECTED]
Web: http://www.nuigalway.ie/geography/gis
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Edzer J. Pebesma
Sent: 02 January 2007 16:28
To: Isobel Clark
Cc: Mehari Tekeste; ai-geostats@jrc.it
Subject: Re: AI-GEOSTATS: Re: Kriging using Nugget Model
For block kriging yes, for point kriging the prediction (standard) error
will be close to sigma.
And happy new year to everyone on ai-geostats!
--
Edzer
Isobel Clark wrote:
Mehari
If you use a semi-variogram which is just nugget, the kriging estimate
will be the arithmetic mean of the sample values and the standard
error will be the standard sigma/root n of classical statistics.
Isobel
*/Mehari Tekeste [EMAIL PROTECTED]/* wrote:
Can I get some suggestion on this issue?
How good is estimation using kriging system that uses a nugget
model? I
have a geodata and the semi-variogram was best fit using a nugget
model
(with semi-variance=C_0; where C_o is a nugget for all h (lag
distance)
values.
Thanks.
Mehari Z. Tekeste, Ph.D.
Research Scientist
Department of Agriculture
Western Kentucky University
1906 College Heights Blvd. #41066
Bowling Green, KY 42101-1066
Phone: 270-745-5972
Fax: 270 745 5972
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and
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fowl of
the air, and over the cattle, and over all the earth, and over
every
creeping thing that creepeth upon the earth. Genesis 1: 26.
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