Define what you mean by non-parametric Monte Carlo please. The basic
Stata tools you want to check out are -bootstrap- and -simulate-.
If all you want to do is to figure out the differences in sampling
with and without replacement, you might be better off looking up some
sampling literature (like Kish or Cochran classic textbooks on
sampling theory) -- sampling without replacement gets estimates that
are more efficient by a factor of 1-n/N where n is the sample size,
and N is the population size. Sampling with replacement is nicer to
deal with analytically.
Note that performing the bootstrap on the survey samples is difficult
-- see Shao (1996, Statistics) review and the original Rao and Wu
(1988, JASA) articles.
On 5/13/07, Carlo Lazzaro <[email protected]> wrote:
Dear Statalisters,
I would like to compare the results of a 10,000-size vector non-parametric
bootstrap simuation performed on the difference of two samples of healthcare
costs drawn at patient-level with the results of a Monte Carlo simulation on
the same dataset.
My main aim would be to detect, if any, the difference between sampling with
and without reintroduction.
Is there any way with Stata 9 to perform a non-parametric Monte Carlo
simulation or should I impose a given distribution (uniform? log normal?)on
the original data set before starting the simulation?
Thanks a lot for Your Kindness and for Your Time.
Kind Regards,
Carlo
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--
Stas Kolenikov
http://stas.kolenikov.name
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