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Re: AI-GEOSTATS: difference between Monte Carlo and bootstrap

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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 1 of 3 , Feb 12, 2004
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      > 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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