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Re: AI-GEOSTATS: Back Transformation : To be or not to be?

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  • Isobel Clark
    Colin Yes the kriging variance is larger but so is your lagrangian multiper. The l.m. is a measure of the inefficiency of your sampling - less sampling (or
    Message 1 of 3 , Sep 13, 2001
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      Colin

      Yes the kriging variance is larger but so is your
      lagrangian multiper. The l.m. is a measure of the
      inefficiency of your sampling - less sampling (or
      badly spaced sampling) big l.m.

      If your kriging variance gets higher than your total
      sill value, you should not really be using the
      estimate anyway (we call this ygiagam).

      Isobel Clark

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    • Isobel Clark
      Colin Yes the kriging variance is larger but so is your lagrangian multiper. The l.m. is a measure of the inefficiency of your sampling - less sampling (or
      Message 2 of 3 , Sep 13, 2001
      • 0 Attachment
        Colin

        Yes the kriging variance is larger but so is your
        lagrangian multiper. The l.m. is a measure of the
        inefficiency of your sampling - less sampling (or
        badly spaced sampling) big l.m.

        If your kriging variance gets higher than your total
        sill value, you should not really be using the
        estimate anyway (we call this ygiagam).

        Isobel Clark

        ____________________________________________________________
        Do You Yahoo!?
        Get your free @... address at http://mail.yahoo.co.uk
        or your free @... address at http://mail.yahoo.ie

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