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RE: Lognormal data. Re: AI-GEOSTATS: Variogram behaviour

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  • Ruben Roa
    ... Digby: Isobel has responded to your questions about my comments, but i may add something of value ... not ... Maximum likelihood estimators. The least
    Message 1 of 3 , Mar 18, 2001
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      >===== Original Message From "Digby Millikan" <digbym@...> =====

      Isobel has responded to your questions about my comments, but i may add
      something of value

      >>Regarding lognormal data, if you are only interested in the mean of the
      >>regionalized variable and its confidence interval whithin the region, and
      >>in the spatial mapping itself, you can just use the MLE estimator of the
      >>lognormal mean, the (Finney-)Sichel estimator that you mentioned
      >What does MLE stand for?

      Maximum likelihood estimators. The least square estimators, such as those
      normally used in goestatistics, are MLE when the distribution of errors is
      Gaussian, so that least square estimators are a particular case of MLE. The
      (Finney-)Sichel lognormal mean is a case of MLE, though only approximate.

      >>to obtain the point estimate,
      >Are you saying to use sichel mean of local data to estimate the grade in
      >block? i.e. Moving Lognormal Average.

      I'm not familiar with mining or geology. What i say is that if in a region,
      the variable of interest measured at several points distribute lognormally,
      then the Finney-Sichel lognormal mean is the MLE of the mean, and that
      estimate can be used as the mean of the regionalized variable without regards
      to its spatial distribution. In the geostatistical paradigm the equivalent
      value would be the kriged mean. Insofar as the kriged mean is a better
      estimate (more unbiased and with less variance) than the lognormal mean of
      lognormally distributed spatial data, you have done something of value by
      performing the spatial analysis. But even when the kriged mean and the
      lognormal mean are similar in terms of the central estimate and the measure of
      precision, the spatial analysis gives additional products which are not
      obtained from a purely distributional analysis of the data.

      >>and the theory and tables in Land (1975, Tables of
      >>confidence limits for linear functions of the normal mean and variance,
      >>Selected Tables in Mathematical Statistics, vol. III, Am. Math. Soc.
      >>Providence, pages: 385-419) to obtain the asymmetric confidence interval.
      >What is asymmetric? Are you saying to develop a confidence interval as
      compared >to
      >kriging which reports the 95% confidence interval, or the estimation
      >I note that sichel provides 90% confidence interval tables in his original
      paper on
      >the t-estimator found in "Symposium On Mathematical Statistics and Computer
      >Applications" SAIMM.

      The confidence interval of the lognormal mean is asymmetric because the
      distribution is asymmetric. I am not familiar with Sichel's tables but my
      previous reference to Land's (1975) tables refer to tables of the quantiles of
      the lognormal distribution which are needed to build confidence interval
      around the lognormal mean.


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