JV wrote:
"A 95% frequentist confidence interval says that 95% of such intervals
you gather will cover the true, fixed parameter"
If I understood you correctly, u are saying if one would repeat the
experiment 10000 times, 95% of these experiments would result in a
confidence interval that covers the unknown population estimate. I
could be missing something, but that does not seem to say much about
the current confidence interval: the one calculated from this data.
How likely that THIS confidence interval covers the true estimate?
/salah
On Tue, Jun 3, 2008 at 3:42 PM, Verkuilen, Jay <[email protected]> wrote:
> Dan Weitzenfeld wrote:
>
>>>I'm grappling with what the results of Bootstrapping can tell you.
> Let's say I bootstrap from my sample 10,000 times, calculating a given
> statistic, giving me the detail I need to use the 2.5% and 97.5%
> percentiles to construct a 95% confidence interval. What does that
> *mean*? Am I 95% confident that the true value of that statistic is
> within the interval? <
>
> <Bayesian> A bootstrap interval means the same bass-ackwards thing as
> any other frequentist interval estimate.</Bayesian>
>
> In short, you've got it backwards. A 95% frequentist confidence interval
> (gotten however you got it, either from asymptotics or bootstrapping)
> says that 95% of such intervals you gather will cover the true, fixed
> parameter. The parameter is an unknown but fixed constant. The intervals
> are random.
>
>
>>>If so, doesn't that require 100% confidence that my sample is an
> accurate representation of the underlying population?<<
>
> The validity of any inferential procedure depends on your having a good
> sample, or a way to model the sampling situation, as is the case with
> censored data, sample selection models (Heckman) and the like.
>
> JV
>
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