Loading ...
Sorry, an error occurred while loading the content.

GEOSTATS: Cross-validations: summary

Expand Messages
  • Gregoire Dubois
    Greetings again, here is a summary of the answers I received to my question about papers on Cross-validation techniques in spatial statistics. Thanks a lot to
    Message 1 of 1 , Mar 24, 2005
      Greetings again,

      here is a summary of the answers I received to my question about papers on
      Cross-validation techniques in spatial statistics.

      Thanks a lot to Frank Hardisty, Dan Cornford, Tom Nolan, Maciej Tomczak and
      Ana F. Militino

      Papers about Cross-validations:

      1) Owosina, A., U. Lall, T. Sangoyomi, and K. Bosworth, Methods for Assessing
      the Space and Time Variability of Groundwater Data, Utah Water Res. Lab., Utah
      State Univ., 1992.

      Owosina et al. [1992] compare two multivariate kernel regression estimators,
      MARS, LOESS, TPSS and Kriging for reconstructing spatial surfaces from a
      variety of irregularly sampled synthetic (with varying signal to noise ratios)
      and ground water data sets. Model parameters were chosen automatically using
      cross validatory measures in all cases. In terms of RMSE and Mean Absolute
      Deviation, overall algorithm ordering (best to worst) across the data sets was
      TPSS, LOESS, KERNEL, MARS, KRIGING. The differences between the best and worst
      algorithm were dramatic in some cases. Methods for interpolating ground water
      data irregularly sampled in space and time were also illustrated.

      (Found at http://earth.agu.org/revgeophys/lall01/node7.html)

      2) Davis, B.M., 1987, "Uses and abuses of cross-validation in geostatistics,"
      Mathematical Geology, v.19, n.3, p. 241-248.

      It discusses some common misconceptions concerning cross-validation. For
      example, use of statistical criteria supposedly yields an optimal
      semivariogram from among competing models. But Davis states that the
      semivariogram is only "best" with respect to "choice of discrepancy measure,
      partition set size, predictive function, and number of models to be

      3) Isaaks & Srivastava. (1989. Applied Geostatistics) are discussing the use
      of Cross-validations pages 533 & 534. A more applied discussion on CV can be
      found in the pages 352-368.

      4) Ana F. Militino (militino@...) and Lola Ugarte have recently
      submitted a closely related paper titled "Assessing the covariance function in
      geostatistics". The method proposed improves in case of unequally spaced data
      the traditional use of cross-validation in this field.

      Comments on the error measures that should be minimised

      The choice of the error measures will depend on the application and the data.
      If the data is Gaussian then the standard sum of squares (root mean square
      error) is probably a good measure to use, but this depends on the cost
      function which is derived from what one wants to achieve.

      I also received few comments on the use of prior knowledge in defining the
      parameters to be used but more discussion about it is outside the scoop of
      this summary (but welcome of course on the mailing list).

      Thank you again very much for the kind help.

      Best regards


      Gregoire Dubois
      Section of Earth Sciences
      Institute of Mineralogy and Petrography
      University of Lausanne

      Currently detached in Italy


      Get free email and a permanent address at http://www.netaddress.com/?N=1
      *To post a message to the list, send it to ai-geostats@....
      *As a general service to list users, please remember to post a summary
      of any useful responses to your questions.
      *To unsubscribe, send email to majordomo@... with no subject and
      "unsubscribe ai-geostats" in the message body.
      DO NOT SEND Subscribe/Unsubscribe requests to the list!
    Your message has been successfully submitted and would be delivered to recipients shortly.