I have been using Variowin for my variogram analysis. My
covariograms are much better behaved compared to semivariograms, and
I would like to use the covariance to estimate the semivariance.
I understand that under the assumption of strict stationarity, which
is harder to satisfy than the intrinsic hypothesis, semivariance and
covariance are essentially equivalent. Since I have a non-stationary
process I would have expected that my covariograms to be just as bad
as my semivariograms, but this is not the case.
I've read that Variowin uses "non-ergotic"
covariance. Exactly what is this and how does it differ from regular
old covariance? I just want to make sure I understand the
assumptions of using non-ergotic covariance to estimate model
parameters.
Thanks!
Sara Kustron
Boston University Department of Geography
--
* To post a message to the list, send it to [EMAIL PROTECTED]
* As a general service to the users, please remember to post a summary of any useful
responses to your questions.
* To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe
ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND
Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org
