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Re: AI-GEOSTATS: mysterious kriging output

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  • Monica Palaseanu-Lovejoy
    Hi Ruben, thanks so much for the references .... and especially the R routines .... i will look into it. This may really give some good answers to my data -
    Message 1 of 2 , Mar 9, 2004
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      Hi Ruben,

      thanks so much for the references .... and especially the R
      routines .... i will look into it. This may really give some good
      answers to my data - once for all - i hope at least. I think we
      neglect in majority of cases to verify if the data come from one or 2
      (or more) distributions and just apply a transformation and do a
      kriging .... it is just too easy that way ;-))

      Again, thank you so much,

      Monica

      > Exploratory analysis of the frequency distribution of the data (i.e. the
      > aggregated, non-spatial, frequency) could reveal the existence of two (or
      > more) populations. To evaluate the evidence in favour of such an
      > hypothesis, you could compare the hypothesis that the frequency
      > distribution is formed by a mixture of two (or more) specified
      > distributions versus the hypothesis that it is formed by only one. The
      > general topic in statistics is called 'mixture distribution analysis' (not
      > to be confused with 'mixture models'). Useful references are:
      >
      > Everitt & Hand, 1981, Mixture distribution analysis. Chapman & Hall
      > Chen & Chen, 2001, Statistics and Probability Letters 52:125
      > Hawkins et al., 2001, Computational Statistics & Data Analysis 38:15
      > http://www.math.mcmaster.ca/peter/mix/mix.html
      >
      > Some robust regression methods, for example, are based on treating the
      > data as coming from a mixture of two distributions, the main one, and a
      > contaminating distribution.
      >
      > If you conclude that there are two (or more) distributions, then you can
      > compute the maximum conditional probability that any given data point
      > belong to any of the two (or more) distributions, and use this computation
      > to classify data. After this exploratory analysis, you could treat the two
      > (or more) populations differently, if there is evidence for a mixture, and
      > maybe even perform separate geostatistical analyses on the separate
      > populations.
      >
      > I used this general strategy in the analysis of a time series of an index
      > of returns from investments in finantial markets. The strategy was
      > proposed by Hamilton, 1994, Time Series Analysis, Ch. 22, Princeton U. P.
      >
      > Ruben



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