Hello I have a VERY skewed data set that fails tests for normality and log normality. Variograms are OK for th elower percentiles of the set but as one goes above the median the variograms get quite poor. And that is causing me a bit of a headache for Indicator kriging.
I came across a paper by Juang et al (J. Environ. Qual. 2001, 30:894-903) that discussed the use of Rank-order geostats for highly skewed data and had my interest peked. I transformed the data by ranking them, then dividing each rank transformed data point by the total number of data points. And the variogram (omni directional, all data) looked exceptionally well. Enthused, I began reading and searching the archive on ai-geostats but have some questions. 1. Is rank order (as in rank/number of samples) geostatistics known by some other name as there doesnt seem to be too much out there bar a couple of papers? 2. Is n-score geostatistics the same thing? 3. Some people seem to say the rank should be divided by N+1 and others N. Which should it be or have I misunderstood? 4. Juang discusses back transforming the data using a "middle point model". I cannot understand how he has acheived this. Has anyone any experience in back transforming the estimates to concentrations? I remember problems I had before with log transformed estimates and whether or not to add half the kriging variance to the back transformation value and would rather not fall into the same kind of problem. If any one has any info on rank order geostatistics and particularly back transforming, I would be very grateful. Thanks in advance M dowdall + + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
