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AI-GEOSTATS: non-ergotic covariance

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  • Sara Kustron
    I have been using Variowin for my variogram analysis. My covariograms are much better behaved compared to semivariograms, and I would like to use the
    Message 1 of 3 , Mar 13, 2001
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      I have been using Variowin for my variogram analysis. My
      covariograms are much better behaved compared to semivariograms, and
      I would like to use the covariance to estimate the semivariance.

      I understand that under the assumption of strict stationarity, which
      is harder to satisfy than the intrinsic hypothesis, semivariance and
      covariance are essentially equivalent. Since I have a non-stationary
      process I would have expected that my covariograms to be just as bad
      as my semivariograms, but this is not the case.

      I've read that Variowin uses "non-ergotic"
      covariance. Exactly what is this and how does it differ from regular
      old covariance? I just want to make sure I understand the
      assumptions of using non-ergotic covariance to estimate model
      parameters.

      Thanks!
      Sara Kustron
      Boston University Department of Geography





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    • Syed Abdul Rahman Shibli
      ... Non-ergodic covariances account for possibly changing means across the region. Means used are different at h- and h+, so the covariance formula is slightly
      Message 2 of 3 , Mar 13, 2001
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        on 14/03/01 3:36, Sara Kustron at skustron@... wrote:

        > I've read that Variowin uses "non-ergotic"
        > covariance. Exactly what is this and how does it differ from regular
        > old covariance? I just want to make sure I understand the
        > assumptions of using non-ergotic covariance to estimate model
        > parameters.

        Non-ergodic covariances account for possibly changing means
        across the region. Means used are different at h- and h+,
        so the covariance formula is slightly different. Refer
        Isaak/Srivastava's book Applied Geostatistics for the definition.
        It's decidedly more "robust" to non-stationarity.

        Syed


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      • Marco Alfaro
        Dear Sara: Sorry, but the non ergodic variogram is an artifact! If you do not believe to me, see the comments about my paper in NACOG 1996 or in
        Message 3 of 3 , Mar 15, 2001
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          Dear Sara:

          Sorry, but the "non ergodic" variogram is an artifact!

          If you do not believe to me, see the comments about my paper in NACOG 1996 or in Geostatistics, Volume 8, No.2.

          The Mathematical proof is in my paper titled "Acerca del variograma no ergódico", in Spanish (difficult to get, if you wish I can
          send a copy for you).

          I think that is more easy to see an example (in my paper you have more examples):

          Let a line with data (the data is very regular and has a trend) sampled at regular intervals of 1. Tha data are (20 data):

          1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 9, 10, 10, 11, 12

          Compute by hand or by your software the non ergodic and the classical variogram (the classical variogram is also non
          ergodic!):

          Conclusions:

          The behavior of the non ergodic variogram, near the origin is linear, and, for the classical variogram is parabolic.

          The non ergodic variogram has a range and a sill, and, the classical variogram always grows.

          I attach a little Qbasic (or QuickBasic) program you can run.

          Best regards,

          Marco Alfaro.

          Listing of the program:

          n = 20
          DIM z(n)
          m = 0 ' mean
          v = 0 ' variance
          FOR i = 1 TO n
          READ z(i)
          m = m + z(i)
          v = v + z(i) * z(i)
          NEXT i
          m = m / n ' mean
          ' the variance is = the non ergodic variogram in the origin
          v = v / n - m * m
          CLS : SCREEN 12
          WINDOW (-10, -10)-(30, 80)
          LINE (0, 0)-(20, 65), 8, B
          LOCATE 3, 10: PRINT "In red, classical variogram, in green, non ergodic variogram"
          FOR k = 0 TO n - 1
          cov = 0 ' the non ergodic variogram.
          head = 0
          tail = 0
          gama = 0 ' the classical variogram.
          FOR i = 1 TO n - k
          hh = z(i)
          tt = z(i + k)
          head = head + hh
          tail = tail + tt
          cov = cov + hh * tt
          gama = gama + (hh - tt) * (hh - tt)
          NEXT i
          head = head / (n - k)
          tail = tail / (n - k)
          cov = cov / (n - k) - head * tail
          gama = .5 * gama / (n - k)
          CIRCLE (k, v - cov), .1, 2
          PAINT (k, v - cov), 2
          CIRCLE (k, gama), .1, 4
          PAINT (k, gama), 4
          NEXT k
          a$ = INPUT$(1)
          END
          DATA 1,1,2,3,3,3,4,4,5,5,6,6,7,7,8,9,10,10,11,12





          Sara Kustron wrote:

          I have been using Variowin for my variogram analysis. My
          covariograms are much better behaved compared to semivariograms, and
          I would like to use the covariance to estimate the semivariance.

          I understand that under the assumption of strict stationarity, which
          is harder to satisfy than the intrinsic hypothesis, semivariance and
          covariance are essentially equivalent. Since I have a non-stationary
          process I would have expected that my covariograms to be just as bad
          as my semivariograms, but this is not the case.

          I've read that Variowin uses "non-ergotic"
          covariance. Exactly what is this and how does it differ from regular
          old covariance? I just want to make sure I understand the
          assumptions of using non-ergotic covariance to estimate model
          parameters.

          Thanks!
          Sara Kustron
          Boston University Department of Geography

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          Sara Kustron wrote:

          > I have been using Variowin for my variogram analysis. My
          > covariograms are much better behaved compared to semivariograms, and
          > I would like to use the covariance to estimate the semivariance.
          >
          > I understand that under the assumption of strict stationarity, which
          > is harder to satisfy than the intrinsic hypothesis, semivariance and
          > covariance are essentially equivalent. Since I have a non-stationary
          > process I would have expected that my covariograms to be just as bad
          > as my semivariograms, but this is not the case.
          >
          > I've read that Variowin uses "non-ergotic"
          > covariance. Exactly what is this and how does it differ from regular
          > old covariance? I just want to make sure I understand the
          > assumptions of using non-ergotic covariance to estimate model
          > parameters.
          >
          > Thanks!
          > Sara Kustron
          > Boston University Department of Geography
          >
          > --
          > * 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.
          > * To unsubscribe, send an email to majordomo@... with no subject and "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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