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 of the error. But note, as Don said yesterday, a random sample, which is clustered, will also give an unbiased estimate of the variance but with a somewhat larger error of estimation. (of course, there may be reasons not to take all the samples as far from one another as possible - for example to estimate the variogram close to the origin, which is it's most important part - but that is another story). The bit that I disagreed with in your original message was the bit that said
"....giving 999 samples to estimate the variance of the 1 million. This will give a better estimate of the variance you could calculate from the million by the least squares classical method, which is what Isobel was saying"
I understood this to say that you would do better with 1000 (or 999) points that with the full million...if that is not what you meant then, yes, i did misunderstand
Colin
-----Original Message-----
From: Digby Millikan [mailto:[EMAIL PROTECTED]]
Sent: Wed 12/8/2004 7:32 PM
To: ai-geostats
Cc:
Subject: Re: [ai-geostats] Re: Sill versus least-squares classical variance estimate
RE: [ai-geostats] Re: Sill versus least-squares classical variance
estimateColin,
You misunderstood me, the 1 million data is the total unknown dataset. Say
you have a volume in a
mine and it's volume is 1 million 1 metre core samples. You drill the volume
and have a sample set
of 1000 1m core samples. You then analyse the statistics of the 1000 samples
to try and estimate
the variance of the total volume (1 million core samples). So your estimate
of the variance comes
from the 1000 samples. You can plot the variogram of the 1000 samples and
you can also calculate
it's variance. You are trying to estimate the variance of the 1 million
peices of core which you do not
have. So you must decide wether your 1000 sample set is a true
representation of the 1 million.
Our argument is that samples within the 1000 which are clustered together do
not create a good
representation of the true dataset and will create a biased estimate.
Digby
www.users.on.net/~digbym
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