unbiased estimate of the mean
JMP have simply stuck with the 1/N-1 term for denominator instead of correcting...
Best Regards
Colin Daly
-Original Message-
From: Edward Isaaks [mailto:[EMAIL PROTECTED]]
Sent: Fri 10/7/2005 1:26 AM
To: AI-GEOSTATS
Subject: [ai-geostats] How to Estimate
Title: RE: [ai-geostats] variograms of interpolated data
Drink the beer Lise...
the shape of the variogram will generally change.
kriging is z^ = sum (lamdba_i * z_i)
with, in simple kriging case, lambda_i = [C_ij]**(-1) * C_ix
with C_ix being the covariance between i and x. So the weigh
Title: RE: [ai-geostats] variograms of interpolated data
Hi Lise
If you did a Gaussian simulation - you should be able to reproduce the variogram (at least when it is done without conditioning data - the data mess it up to some extent).
But kriging produces a smooth function and, if you c
as scalar product. (This would convince you that there are no restrictions on the weights)
Regards
Colin Daly
-Original Message-
From: Andreas Dominik Cullmann [mailto:[EMAIL PROTECTED]]
Sent: Tue 1/4/2005 3:02 PM
To: ai-geostats@unil.ch
Cc:
Subject: [ai-geostats] the
ened a bit better in matheron's classes on his 'estimating and choosing' book)
Regards
Colin Daly
-Original Message-
From: Meng-Ying Li [mailto:[EMAIL PROTECTED]]
Sent: Wed 12/8/2004 9:52 PM
To: Colin Daly
Cc: Digby Millikan; ai-geostats
Subject: RE:
Title: RE: [ai-geostats] Re: Sill versus least-squares classical variance estimate
Hi Digby
Yes, I agree with what you say below - if your only aim was to estimate the variance and you only could collect 1000 samples - then choose them to be 'maximally independent' to reduce the variance o
Title: RE: [ai-geostats] Re: Sill versus least-squares classical variance estimate
Hi Meng-Ying
27 points - you can't really calculate a variogram. With a range of 3 - you have about 9 correlation lenghts in the field. So as a crude approximation, even the standard deviation on the estimate
Title: RE: [ai-geostats] Re: Sill versus least-squares classical variance estimate
Hi Digby
Sorry to say - but suggesting that less data is systematically better is mistaken - this is fundemental...and is contained in the intro pages of any good intro to geostats. If the data is clustered
Title: RE: [ai-geostats] Sill versus least-squares classical variance estimate
Meng-Ying
samples taken beyond the range are, in fact, far enough apart from one another! The sill is - to all intents and puposes - equal to the variance of the data (This fails if there are trends in the data
mber of 'independent' samples would be to take the lenght of the field divided by range (provided that we have enough sample data to cover the field at a sampling spacing less than the range). This could be used in place of the raw number of samples n - which as said before will give a
However by construction of the random function, the mean is not different. We have been lulled into the false conclusion of differing means by assuming that all our data are independent.
Regards
Colin Daly
-Original Message-
From: Chaosheng Zhang [mailto:[EMAIL PROTECTED]]
Sent: Sun 12
d so cannot 'prove' that the samples come from the same random function - even if they do.
Regards
Colin Daly
-Original Message-
From: Glover, Tim [mailto:[EMAIL PROTECTED]]
Sent: Fri 12/3/2004 3:15 PM
To: Colin Badenhorst; [EMAIL PROTECTED]
Cc: [EMAIL PROTECTED
Title: problem of spatial continuity of groundwater head
Kai
There has been some work in this area
there are some references at the Ecole des Mines, Centre de Geostatistique
website see the papers by Anne Dong and Chris Roth to start with
http://www.bib.ensmp.fr/cg
point)
Regards
Colin Daly
- Original Message -
From: "Soeren Nymand Lophaven" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, January 30, 2003 3:22 PM
Subject: AI-GEOSTATS: Observations with a known standard deviation
Dear list
I am currentl
the best book
that I have read on the maths of 2 point random functions (ie characterisaed
by a covariance fnction or generalised covariance function or variogram)) is
Yaglom A.M., 1987 Correlation Theory of Stationary and Related Random
Processes Vol 1 Springer-Verlag.
Best Regards and happy
lication. "The
intrinsic random functions and their applications Adv. App. Prob., 5, pp
439-468"but beware the maths is not simple in Matheron's paper)
Regards
Colin Daly
- Original Message -
From: "Isobel Clark" <[EMAIL PROTECTED]>
To: <[EMAIL PROT
he problem is that it has quadratic behavior at the origin - so you might
need to add a small spherical or exponential variogram as suggested by
Isobel to ensure that your resultant random function model is not
differentiable.
Bye
Colin Daly
- Original Message -
From: <[EMAIL PROTECTED]&g
- I don't use the UK pronunciation
- rather the one suggested by Gavin from Cape Town(ggaa).
Bye for now
Colin Daly
- Original Message -
From:
Marcus Schneider
To: [EMAIL PROTECTED]
Sent: Tuesday, January 29, 2002 5:34
PM
Subject: AI-GEOSTATS: Pronunciation
al kriging option in your kriging program - although
you will not get kriging variance values in this case.
I can't be sure what 'no search' means - you will have to look it up in the
software manual - but my guess is that it refers to unique neighbourhood if
you are seeing smoo
y become too
large I would be tempted to live with the small negative estimates and just
correct them to zero.
Best Regards
Colin Daly
p.s. I have just 'grabbed' some references for
this stuff from the web at Melanie Wall's site http://www.biostat.umn.edu/~melanie/ -
I
found in the usual way) and your weights must satisy
sum_i
lambda_i = 0
sum_i mu_i =
0
Best Regards
Colin Daly
- Original Message -
From:
Bernard Pelletier
To: [EMAIL PROTECTED]
Sent: Monday, July 16, 2001 11:15
PM
Subject: AI-GEOSTATS: (
indicator models. STORM also allowed for uncertainty
modelling and geological scenario management - these are reflected in the
current versions of RMS.
Have a look at the site above, and if you
want more information, we can talk offline if you wish
Regards
Colin Daly
- Original Message
void the gaussian. The problems run deeper than just a little
bit of numerical round off.
regards
Colin Daly
Chris Lloyd wrote:
Hello, I
am emailing the list concerning an important issue about which I have read
conflicting views. The use of the Gaussian variogram model is widely considered
unwi
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