There ws a pretty good paper on global standard errors
in the 1984 APCOM proceedings, so I am sure it should
be in the major textbooks by now.

Commparing the sills is very straightforward, I think.

Isobel
http://geecosse.bizland.com/books.htm

 --- Chaosheng Zhang <[EMAIL PROTECTED]>
wrote: 
> Isobel,
> 
> Good idea, and that's a step forward. Any references
> or is it still an idea?
> 
> Cheers,
> 
> Chaosheng
> 
> ----- Original Message ----- 
> From: "Isobel Clark" <[EMAIL PROTECTED]>
> To: "AI Geostats mailing list" <[EMAIL PROTECTED]>
> Sent: Monday, December 06, 2004 1:07 PM
> Subject: Re: [ai-geostats] F and T-test for samples
> drawn from the same p
> 
> 
> > Dear all
> >
> > I am having difficulty understanding why none of
> you
> > want to try a spatial approach to statistics.
> Everyone
> > is trying to make the 'independent' statistical
> tests
> > work on spatial data. Try turning this around and
> look
> > at the spatial aspect first.
> >
> > (1) Testing variances: the sill on the
> semi-variogram
> > (total height of model) is theoretically a good
> > estimate for the sample variance when
> auto-correlation
> > or spatial dependence is present. Do your F test
> on
> > that. Yes, you still have degrees of freedom
> problems,
> > but with thousands of samples the 'infinity
> column'
> > should be sufficient.
> >
> > (2) Testing means: the classic t-test in the
> presence
> > of 'equal variances' requires the 'standard error'
> of
> > each mean. For independent samples, this is
> s/sqrt(n).
> > For spatially dependent samples, this is the
> kriging
> > standard error for the global mean. Your only
> problem
> > then is getting a global standard error.
> >
> > Isobel
> > http://geoecosse.bizland.com/whatsnew.htm
> >
> >
> 
> 
>
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