With five to six samples per population, concluding
anything from the tests would really be pushing it.
Complementing the results with any deterministic
knowledge of the underlying population (genesis,
noteworthy features, prior experience, etc) could lend
some measure of validity to what you wi
on 23/02/01 17:14, Gregoire Dubois at [EMAIL PROTECTED] wrote:
> the normal score transformation set seems to be, at least in the litterature,
> the magic solution to handle a skewed data set. Could anyone point me the main
> drawbacks of such a step ?
Decisions, decisions. Some variables are in
on 26/02/01 15:45, L. Wiles at [EMAIL PROTECTED] wrote:
> Summary:
> What is the interpretation of a directional correlogram that does not
> reach the sill?
Either you have a "drift" or trend (non constant mean) or you truly have
a variable with an infinite capacity for dispersion (fractal
model
on 14/03/01 3:36, Sara Kustron at [EMAIL PROTECTED] wrote:
> I've read that Variowin uses "non-ergotic"
> covariance. Exactly what is this and how does it differ from regular
> old covariance? I just want to make sure I understand the
> assumptions of using non-ergotic covariance to estimate mo
on 16/03/01 2:24, Sara Kustron at [EMAIL PROTECTED] wrote:
> It appears that this technique is computationally inaccessible to us
> non-programmers at this point in time. Could it be argued that though
> theoretically questionable non-ergodic covariance has some practical value
> in that it succ
Almuth Wameling wrote:
>
> conditional to the observed sample. This conditional process is not
> stationary and its covariance structure is not the structure of the
> unconditional process any more (cf. Cressie, 1991). However, in
> many papers and books the quality of simulations is judged
> acc
d. 16/04/01 17:44 skrev [EMAIL PROTECTED] på
[EMAIL PROTECTED]:
>
>
> Dear Colleagues
>
> I have learned that a valid variogram should be an even function, i.e.
> gamma(h)=gamma(-h). However, this condition is not satisfied by
> exponential variogram function. Am I missing something in this ar
>From: "McKenna, Sean A" <[EMAIL PROTECTED]>
>
>1) When trying to explain the concepts of spatial variability and
>uncertainty, we have found that showing example realizations of what the
>possible distribution of contaminants could look like provides the groups
>involved to get a more intuitive
Variogram modeling is usually a pre-requisite for
kriging and/or stochastic simulation. It's not
usally something that you'd want to "automate" in some
sort of computer program. Selection of a model type/range/sill
will usually be based on available sample points,
or analagous samples of the sam
It seems that you can extend this further to calculate a madogram
for each grid density (mean absolute difference). Different grid
densities, different radii, ergo different madogram shapes. Each madogram
may or may not show a range. The trick is to come up with a grid
density that would result
One way is to generate unconditional fields using
simulated annealing. Refer the GSLIB textbook for details.
One can specify a user defined variogram and histogram.
Syed
Original message
>Date: Wed, 28 Aug 2002 10:14:54 +0200 (METDST)
>From: Soeren Nymand Lophaven <[EMAIL PROTECTED]>
If the skewness of the fish data is causing havoc to your
variograms try one of the more "robust" measures, i.e.
the family of relative variograms (general/pairwise), or the
non-ergodic covariance. Transformation would mask the extreme
values which may or may not be very significant to your
prob
On 29/11/02 5:32 AM, "Digby Millikan" <[EMAIL PROTECTED]> wrote:
> Hello,
> I was wondering if someone can tell me about statistical parameters,
> why standard deviation and variance is used as opposed to mean absolute
> deviation from the mean. It rings a bell that intergral calculus has
There a
On 18/12/02 1:01 PM, "Gregoire Dubois" <[EMAIL PROTECTED]> wrote:
> Dear all,
>
> I'm looking for a Windows software able to perform a 2D computation (either
> sandbox or box counting) of the fractal dimension of a monitoring network. The
> method is illustrated in
You can assume a fractional i
On 25/1/03 10:23 PM, "Peter Bossew" <[EMAIL PROTECTED]> wrote:
> Dear listers,
>
> I want to investigate the spatial properties of point patterns which can
> possibly be described as results of Levy Flights, i.e. Brownian motion
> with hyperbolically distributed path lengths. For this purpose I w
I always thought the CV would be more useful as a means to compare two
different distributions with dissimilar means. Two CVs measured at two
different locations in the farm will indicate the relative dispersion
between the two locations, since both standard deviations would be
normalized by th
If you have gridded observed data at close and regular spacing for the block, then why
bother with a Gamma(V,V)? Just calculate the variance for the block directly.
On the one hand you need "a spatially dependent estimate of the whole plot/block
variance" but on the other hand you want to disp
Just to echo Pierre and Ed's remarks, I think it is easy to be trapped in "analysis
paralysis" when it comes to variogram modelling. It's also easy to lose sight of the
goals of the interpolation process, and also the inherent limitations of working with
experimental variograms. In some proble
Not sure how anisotropic "fractal" spatial correlation models would fit in the whole scheme of things. You're essentially assuming a power law model (Brownian motion) to model the spatial correlation, which implicitly assumes a phenomena with an infinite capacity for dispersion, i.e. no range. The
dimension have any reasonable physical meaning?
Any experience with this?
Thanks again for the kind help.
Gregoire
-Original Message-----
From: Syed Abdul Rahman Shibli [mailto:[EMAIL PROTECTED]
Sent: 16 July 2004 19:23
To: Gregoire Dubois
Cc: [EMAIL PROTECTED]
Subject: Re: [ai-geo
Dear List members,
Yes, this might be statistical heresy but would anyone be able to
provide any references that give a probabilistic treatment (i.e.
instead of just the sample estimator) of the general family of relative
variograms, in particular the pairwise relative and the general
relative.
Perhaps there is some confusion here. Simple kriging, for instance, can be
decomposed to the familiar multilinear regression equation since if one
assumes all the Z(Xi)s are independent variables, then in the covariance
matrix C all of C(Xi,Xj) would be zero except for C(Xi,Xi). So
LiC(Xi,Xi)
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