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
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
: Re: [ai-geostats] Re: Sill versus least-squares classical
variance estimate
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
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
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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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
I understand why it is not appropriate to force the sill so it matches the
sample variance. My question is, why estimate the overall variance by the
sill value when data are actually correlated?
Meng-ying
On Tue, 7 Dec 2004, Isobel Clark wrote:
Meng-Ying
We are talking about estimating the
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