Hi Wolfram, You forgot to mention what you want to do with these data. If the objective is to perform kriging, then you can use either kriging with nonsystematic errors or soft indicator kriging to account for the variable level of reliability of your data.
Cheers, Pierre <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Dr. Pierre Goovaerts President of PGeostat, LLC Chief Scientist with Biomedware Inc. 710 Ridgemont Lane Ann Arbor, Michigan, 48103-1535, U.S.A. E-mail: [EMAIL PROTECTED] Phone: (734) 668-9900 Fax: (734) 668-7788 http://alumni.engin.umich.edu/~goovaert/ <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> On Mon, 29 Nov 2004, Wolfram Ruehaak wrote: > Dear all, > > I have difficulties to find information's about the following problem. > > I have a lot of spatially scattered measurements. These measurements > have - resulting from different measurement methods - different > measurement errors, which are known. > > For example some have an total error of 5%, some of 10% and a third > group of 20%. > > I want to give these values a quality-weight in the range from 0.0 to > 1.0. (In this case three different weights.) > > How can I do this? > > Simple is a weight = 0 which is a value so bad I don't want to use it, > and a weight = 1 which could be the value for the group with the best > measurements (in this case error = 5%). > > Is there a statistically firmed way to quantify the weights. > > Any suggestions will be very welcome. > Is there any literature that discusses this matter? > > Thanks in advance. > > Wolfram Ruehaak > > >
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