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AI-GEOSTATS: difference between Monte Carlo and bootstrap

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  • Monica Palaseanu-Lovejoy
    Hi everyone, I would appreciate any light in defining and separating Monte Carlo resampling techniques and bootstrapping techniques. In my search it seems that
    Message 1 of 3 , Feb 12, 2004
      Hi everyone,

      I would appreciate any light in defining and separating Monte Carlo
      resampling techniques and bootstrapping techniques. In my search
      it seems that the two notions are more or less the same .... which
      puzzles me - i suppose if there are 2 different names, would be 2
      different definitions - but maybe i am wrong.

      Also, i am interested in calculating prediction intervals for kriging.
      Any reference will be much appreciated. until now i have read only
      "the bootstrap and kriging prediction intervals" by Sara Sjostedt-De
      Luna and Alastair Young, published in 2003 in Scandinavian
      Journal of Statistics.

      If you know about any software which would calculate the kriging
      prediction intervals - would be super.

      Thank you in advance for any help,

      Monica

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    • Ruben Roa Ureta
      ... In bootstrapping you create samples which have not been observed directly from samples which have been observed, while in Monte Carlo you create samples
      Message 2 of 3 , Feb 12, 2004
        > Hi everyone,
        >
        > I would appreciate any light in defining and separating Monte Carlo
        > resampling techniques and bootstrapping techniques. In my search
        > it seems that the two notions are more or less the same .... which
        > puzzles me - i suppose if there are 2 different names, would be 2
        > different definitions - but maybe i am wrong.

        In bootstrapping you create samples which have not been observed directly
        from samples which have been observed, while in Monte Carlo you create
        samples which have not been observed indirectly from a model for samples
        which have been observed. In bootstrap an observed sample is turned into a
        finite population from which to re-sample, while in Monte Carlo a model
        derived from an observed sample is used to produce an infinite set of
        populations from which to re-sample.
        There are 3 major resampling techniques: bootstrap, Monte Carlo, and
        randomization (aka permutation tests). In the latter, you create samples
        which have not been observed by swapping the indices for the data in
        samples which have been observed instead of creating entirely new data.
        One problematic question which is made transparent by resampling
        techniques, but which also applies to most of statistical inference, is:
        would you base your inference on data which have not been observed?
        R.

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