Simone
Under the intrinsic hypothesis you can have a semi-variogram (bounded or unbounded) if the data is non-stationary.
Generally we assume a stationary mean when calculating a semi-variogram to simplify the calculation. If the mean is not stationary, you have to include a drift or 'trend' in your calculation.
The data has to be stationary in the mean to have a covariance function simply because you have to subtract the mean to get a covariance. This is theory.
In practice you can always calculate the covariance, you just assume a constant mean. This does not guarantee that it is in any way meaningful.
If the mean is not stationary, you will get a parabola added to your 'real' semi-variogram graph. This is the universal sign of significant drift or trend. Your semi-variogram can be unbounded without going parabolic - see, for example, linear and power models.
If the variance of the data is non-stationary, you may still have intrinsic stationarity. The usual example used in the text books is a 'random walk' or Brownian motion. The test for intrinsic stationarity is obtaining a meaningful semi-variogram when you calculate it. You can also try jack-knifing - leave out some of your data and see if the semi-variogram still looks similar.
Isobel
http://www.kriging.com (under construction)
* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm )
* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
