Dear Isobel,
Thanks for the information. Perhaps I didn't explain my request clearly.
What I need is to verify the ideas you suggested in the previous message.
Specifically, (1) Has anybody used the sill values (in geostatistics) to
replace the variances (in classical statistics) in F test? (2)
Digby
I see where you are coming from on this, but in fact
the sill is composed of those pairs of samples which
are independent of one another - or, at least, have
reached some background correlation. This is why the
sill makes a better estimate of the variance than the
conventional statistical
Hi Isobel,
Could you explain why it would be a better estimate of the variance when
independance is considered? I'd rather think that we consider the
dependance when the overall variance are to be estimated-- if there
actually is dependance between values.
Or are you talking about modeling sill
Meng-Ying
We are talking about estimating the variance of a set
of samples where spatial dependence exists.
The classical statistical unbiassed estimator of the
population variance is s-squared which is the sum of
the squared deviations from the mean divided by the
relevant degrees of freedom.
The sample variance (assuming that you use the n-1 divisor) is an
unbiased estimator of the population variance provided you use random
sampling. Note the ing on the word sampling, it is not quite correct
to talk about random samples or independent samples. or at least it
may be mis-leading.
It was previously mentioned that a common approach is to subdivide populations
into those of equal mean and variance so that stationarity is obeyed.
What do you suggest as tests for determining equivalence of mean and variance
prior to spatial analysis?
Thanks,
Randy.
* By using the
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
Title: RE: [ai-geostats] Continuing discussion on F and t tests
I'd agree with Don's point about the sample variance being unbaised under random sampling. Because of the linearity of the estimate, the lack of independence of samples is not a problem here. This should not be confused with
Thanks Donald,
I think what you mean by adequately is the sampling with CSR (complete
spatial randomness) -- please correct me if I'm wrong. But I still have
problem about estimating the variance. I mean, even if we sample with
CSR, wouldn't the sample variance still be smaller than the sill
Rajive,
Cyclic variograms indicate that your attribute of interest also fluctuates.
I encountered this when working with time-series of water levels, in which
case the fluctuations were related to seasonality. I am not sure what it
would mean in the case of platinum deposits. Such variograms
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
Dear Rajive:
I cannot conclude with only 328 pairs that the feature is "wavy" because I do not know how those pairs are distributed for each point in the variogram. Try different lag spacings, or create an "equal-n" lag variogram where each lag has the same number of pairs. If that shows the
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