Re: st: Bootstrapping question

[Austin Nichols <austinnichols@gmail.com>]

Bootstrapping is designed to improve CIs by making their coverage
better, not by making them smaller! A better approach for your case
would be Bayesian, but again a better CI does not necessarily mean a
smaller CI. Some priors might result in smaller credible intervals,
but others in larger, and you need to describe the dependence of your
results on your assumptions. If you estimate 15 ways and only report
the one that accords with the conclusion you want, you are committing
scientific fraud.

For the multinomial case, a flat prior is not the face of a simplex:
see also section 5.2 in http://www.tilman-neumann.de/docs/BIEP.pdf

Depending on how serious you are about getting the smallest possible
CI, you may need to do a lot of reading about maximum entropy methods.

On Thursday, February 7, 2013, Ilian, Henry (ACS) wrote:
Nick, you're right. Some of the potential (and actual) outcomes have
observed zeros. I looked everywhere I could think of for the formula
to compute sample sizes for multiple categories but couldn't find it.
In the process, I read somewhere that the problem of multiple
categories reduces to a two-category problem. I asked one statistician
about this, and he said not true and suggested I take his on-line
advanced sampling class. I certainly am considering that, but for the
meanwhile, I still have a sample size that results in very wide
confidence intervals. Again, my understanding from reading about
bootstrapping is that one of the things bootstrapping was designed to
do was to improve estimates of confidence intervals in small samples.
My question is, can I use it in this situation, and if I do, what do I
report?
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