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AI-GEOSTATS: non-normal data distribution

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  • Jonathan Seaquist
    Dear Members, I have been reading the archives on dealing with non-normal data distributions in kriging with great interest, yet a complete understanding of
    Message 1 of 1 , Feb 5, 2001
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      Dear Members,

      I have been reading the archives on dealing with non-normal data
      distributions in kriging with great interest, yet a complete understanding
      of the normal-score transform (or rather the back-transform) eludes me
      still, and I could not find a complete treatment of issues concerning the
      back transform.

      I am kriging rainfall data for the West African Sahel (n=45) - a rather
      sparse data set. The data are not normally distributed, and do not appear
      to be log normal either. Stratified kriging is not a good idea because of
      the small sample size. Taking the square root seems to fix the
      problem. However, from what I gather from previous discussions,
      back-transforming the kriged data is *not* a good idea. One way to
      overcome this, as I understand, is to normal-score transform the raw data,
      perform the kriging, and then back transform from normal scores. How is
      this different than back-transforming from a log or square root
      transformation : are there still problems with bias in the esimtates?

      Best wishes,

      Jonathan

      --------------------------

      Jonathan Seaquist
      Dept. of Physical Geography
      Lund University
      Sölvegatan 13
      S-223 62 Lund
      Sweden

      phone: 46 46 222 4262
      fax: 46 46 222 4011
      email: jonathan.seaquist@...


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