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Re: st: Comparing non-parametric bootstrap vs. Monte Carlo
--- Carlo Lazzaro <email@example.com> wrote:
> 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?
I understand from your question that you want to compare the bootstrap,
which draws random samples from your data with replacement, with
another Monte Carlo simulation which draws from a non-parametric
representation of your data without replacement.
What makes the bootstrap non-parametric is that it immediately draws
from the observed data, so it won't need a model (with parameters) to
draw its replications. The data itself is a non-parametric
representation of itself (and hopefully the population). In order to
get estimates for standard errors you need to make sure that the
bootstrap replications have the same number of observations as the
original data. In your Monte Carlo implementation you would draw
samples from your data without replacement. In that case you would
reproduce the original dataset every time you draw the sample. So that
won't work. In other words, if you want to sample without replacement
you will have to impose a model.
Hope this helps,
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
1081 HV Amsterdam
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
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