Mark,
this really depends on how you want to estimate the parameters of the covariance / variogram. If you want to use maximum likelihood, then due to the need to invert a matrix, which is O(n^3), generally sizes above about 1000 become rather prohibitive on a desktop computer. One possible alternative that attempts to retain statistical rigour but scale gracefully with sample size is our Sparse Sequential method:
http://www.ncrg.aston.ac.uk/~csatol/ogp/index.html
Alternatively you could use methods of moment estimators (i.e. the classic sample variogram) and fit these empirically using some function. Note that in computing the sample variograms one can work in a sequential fashion, so that not all pair comparissons need be stored, but they must be computed .... so it will be slower, scaling as O(n^2) in the computation of the sample variogram.
cheers
Dan
Mark Coleman wrote:
Greetings,
I am coding some basic geostatistical procedures and was curious about the "typical" sorts of sample sizes researchers run into. I know that sizes of n=1000 are fairly common. How about sizes of N=10,000 or greater? Are variograms computed on samples this large?
Thanks,
-mark
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Dr Dan Cornford [EMAIL PROTECTED] Computer Science Aston University Aston Triangle tel +44 (0)121 204 3451 Birmingham B4 7ET fax +44 (0)121 333 6215
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