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
Rajive
I haven't read the other responses yet, so this may be
redundant.
Two possibilities:
(1) anisotropy: if this is shallow marine data there
should be a difference between longshore drift and
off-shore deepening of sea-bed. You have an
omni-directional semi-variogram. It is possible that
Thanks Digby,
You answered more to the question I asked. In this case I assume that you
define the overall variance of a random field to be the variance of data
spaced beyond the variogram range-- which I can buy, but not quite sure
if this definition is practical in all cases-- and that's why I
Dear Meng-Ying,
It's not that you are defining variance to be the variance of data to be
data
beyond the range of the variogram. Say you have a panel made up of a
1 million samples which covers the entire panel, then you select 1000
samples
to estimate the variance. If two samples of the
Dear Meng-Ying,
If you imagine the 1 million samples (total dataset and area) overlying
a pattern of 1000 low and high grade regions, your 1000 sample set
you would only want one sample from each low grade and each high
grade region, if you had two samples in one low grade region, this
region
Hole effect model, usually means your deposit has
alternating high and low grade zones. Sorry I'm not
familiar with the geology of this deposit but examples
of this could be pods of high grade spaced apart from
each other with waste or low grade halos between
them. If only the high grade zones
Meng-Ying
No, I do not think we are communicating.
The variance of data values is not affected by
correlation between the sample values.
The estimated variance for the population IS affected
by correlation between the sample values. Statistical
inference about the population is based on the
Hi Digby and All,
I did a little experiment on the idea that Digby mentioned: The sill will
estimate the population variance, but found it not true in my experiment:
1. I generated a set of one-dimentional data with 27 points on regular
unit spacings, which I'd like to take it as the true, or
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
Mat,
The point is the spatial randomness with which they were sampled. Typically
in a mining situation core samples far from follow a spatially random
sampling
pattern.
Digby
Geolite Mining Systems
www.users.on.net/~digbym
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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
Hi Colin,
What I'm talking about in my example is comparing two descriptive
statistics for this population which consists of 27 data points. No
estimation here is involved, so the thing about confidence interval of
the mean or variance is not of concern here. And it doesn't matter which
model I
And just a personal opinion, I would like to think
geostatistic
theories apply to population of any size, as small
as 27, or as large as
1,000,000. If I'm making an example that
geostatistics doesn't apply, then
there's something to concern about in this approach.
Geostatistics applies to
Isobel,
I agree with you that no estimation is needed if we have the population,
and that's what I said in the beginning of my last discussion. I'm saying
that when variance and sill in a population doesn't match, I'll have
concern when I have to use sill in a sample to estimate the
population
Title: RE: [ai-geostats] Re: Sill versus least-squares classical variance estimate
Hi Meng-Ying
The calculation of the experimental variance on a finite set of data (population or sample) is simply a mathematical operation
- in itself it has no more meaning that say adding the square of
Meng-Ying,
For interests sake could you perform the same experiment for a stationary
sample set of size 1000.
Regards Digby
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On Thu, 9 Dec 2004, Digby Millikan wrote:
Meng-Ying,
Even if your population variance and sill do not match identically,
the sample sill should still be a better estimate than the sample
variance,
when you consider the amount of clustering which occurs in sampling.
Digby
All right, if
Meng-Ying,
For interests sake could you perform the same experiment for a
stationary sample set of size 1000.
Regards Digby
I did that. But with this short influence range of just 3 lags in a
population of size 1000 (0.3% of the domain), the correlation of data
doesn't do much influence to
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