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 and/or the data is somehow preferentially clustered in high or low regions).
If you need further confirmation - you will find this developed in the first pages of most of the standard geostat books - such as the 2 classics...
1) Matheron. "The theory of regionalised variables..." ( also known as Fasicule 5)
2) Journel and Huijbrets "Mining Geostatistics"
But you will almost certainly find it in later books like a) Isaacs and Srivastava; b) Goovaerts c) Chiles and Delfiner
-----Original Message-----
From: Meng-Ying Li [mailto:[EMAIL PROTECTED]]
Sent: Tue 12/7/2004 11:26 PM
To: Isobel Clark
Cc: ai-geostats
Subject: Re: [ai-geostats] Sill versus least-squares classical variance estimate
Dear List,
I think I'd like to state my problem more clearly.
What I think to be the estimate of the overall variance is the expected
variance in the future samples. This have to do with what kind of sampling
scheme we use in the future, however.
If we could assume the future samples to be enough apart from each other,
then I'd have no problem using the sill value we calculated from the
experimental variogram. Or, if we're talking about setting up a standard
value so we could compare the maximum possible variances to that of other
samples, I'd also have little doubt on the estimation using the sill
value. Otherwise I think the sill value would be generally an
over-estimation of the variance for a future sample, even for samples
collected with complete spatial randomness in the future.
And again, please correct me if I missed any important point along the
discussion. I'd really like to be careful about (geo)stats, but probably
not as careful about asking questions.
Mng-yng
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