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Re: AI-GEOSTATS: curve fitting summary

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  • Isobel Clark
    ... I hate to sound ignorant here, but aren t most of the standard semi-variogram models polynomials of one kind or another? I remember seeing a paper a few
    Message 1 of 5 , Nov 21, 2002
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      > To be a valid covariance function, it must be
      > positive definite (as a function). In particular
      > this implies that the function is bounded
      > (hence no polynomials)
      I hate to sound ignorant here, but aren't most of the
      standard semi-variogram models polynomials of one kind
      or another?

      I remember seeing a paper a few years ago by a coupl
      eof blokes from Pretoria University on a generalised
      polynomial fit which would be positive definite. I
      don't have it to hand but can probably track it down
      if given sufficient motivation ;-)

      Isobel Clark
      http://geoecosse.bizland.com/news.html

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    • Colin Daly
      I think that Pierre and Don Myers gave the correct response. For beginners the lesson must be that you cannot use any old function (and in particular a
      Message 2 of 5 , Nov 22, 2002
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        I think that Pierre and Don Myers gave the correct response. For beginners
        the lesson must be that you cannot use any old function (and in particular a
        polynomial) as a covariance function. It will lead to singular kriging
        matrices for some configurations of data points. That is why we stick to a
        few tried and tested covariance functions in most computer packages. In fact
        there are lots of 'esoteric' covariance functions known. You don't have to
        just stick to the spherical, exponential and Gaussian ( it might be an idea
        to have a list of covariance functions somewhere on the AI geostats site)

        To get after the point that Isobel said about polynomials - the facts are
        best stated in terms of the type of random function

        If the random function is stationary - then a covariance function C(h)
        exists and is bounded - C(h) is a positive definite function so absolutely
        no polynomials allowed whatsoever!

        If the data is from an intrinsic function of order 0 (IRF-0) then the
        variogram must have growth less than h**2, so the only polynomial is of
        the form "gamma(h) = a+b*h where b>=0" (Of course you can have "gamma(h)
        = h**c where c<2 but the only polynomial is with c=1)

        If the data has more general nonstationarity such as the IRF-k, then the
        generalised covariance can be a polynomial. However
        1) the polynomial is not arbitrary (there are constraints on the
        coefficients)
        2) it is definitely not found by fitting a curve to the experimental
        variogram - you need special fitting techniques which are not found in many
        packages


        In other words, if your variogram appears to grow without bounds, you have
        got non staionarity. You can either try a linear model (if appropriate) or
        try to tackle the non-stationarity head on by removing the trend from your
        data and then using ordinary variogram fitting techniques to the stationary
        residuals (or by applying an IRF-k model if you have the software)

        For more details, see the book by Chiles and Delfiner for example (or for
        the real purist you can go to Matheron's original publication. "The
        intrinsic random functions and their applications Adv. App. Prob., 5, pp
        439-468" but beware the maths is not simple in Matheron's paper)


        Regards


        Colin Daly



        ----- Original Message -----
        From: "Isobel Clark" <drisobelclark@...>
        To: <ai-geostats@...>
        Sent: Thursday, November 21, 2002 10:23 AM
        Subject: Re: AI-GEOSTATS: curve fitting summary


        > > To be a valid covariance function, it must be
        > > positive definite (as a function). In particular
        > > this implies that the function is bounded
        > > (hence no polynomials)
        > I hate to sound ignorant here, but aren't most of the
        > standard semi-variogram models polynomials of one kind
        > or another?
        >
        > I remember seeing a paper a few years ago by a coupl
        > eof blokes from Pretoria University on a generalised
        > polynomial fit which would be positive definite. I
        > don't have it to hand but can probably track it down
        > if given sufficient motivation ;-)
        >
        > Isobel Clark
        > http://geoecosse.bizland.com/news.html
        >
        > __________________________________________________
        > Do You Yahoo!?
        > Everything you'll ever need on one web page
        > from News and Sport to Email and Music Charts
        > http://uk.my.yahoo.com
        >
        > --
        > * To post a message to the list, send it to ai-geostats@...
        > * As a general service to the users, please remember to post a summary of
        any useful responses to your questions.
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        "unsubscribe ai-geostats" followed by "end" on the next line in the message
        body. DO NOT SEND Subscribe/Unsubscribe requests to the list
        > * Support to the list is provided at http://www.ai-geostats.org


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