Hi, all: I would appreciate your comments on following approach:
Suppose that I have n data points s(l), l =1,..n. If I like to test whether the mean is zero, traditionally I would take the sample mean, sbar, and sample variance, say sigma1sbar, to use the t-test, such as t=sbar/sigma1sbar. However, if the data is observed from 2-d space and the data are spatially correlated, then this t-test would inflate the type-I error. What I propose to do is to use the variogram model to find the correlation among data points and then the sigma2sbar would be then calculated from the covariogram. In this case, the sigma2sbar would be always bigger than the sigma1sbar since it incorporates the spatial correlation and the t-test would tend to accept the null hypothesis. Any comments and suggestions for this approach? Any references? Din -- * 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
