2
$\begingroup$

I'm moving more and more to bootstrap for my analyses to estimate the variability of parameters of glm regression models.

I usually report the bootstrap estimate of the parameter (the mean of the bootstrap distribution), but from the theory I know that the bootstrap estimate should approach the original estimate if all $n^n$ possible resamples are evaluated to build the distribution.

So I started to wonder that maybe in the results I should report the original parameter instead of the bootstrap one, together with the bootstrap derived variability statistics (eg, BCa CIs)? Or maybe the bias corrected estimate ($2*\theta-\theta^*$)?

What is the common place and the right thing to do from your experience?

$\endgroup$
0
$\begingroup$

Bootstrapping is solely for estimating variability/precision not for estimating a point value. You should use the estimate based on your data and use the bootstrap standard errors or confidence interval to report precision (or p-value if you're using boostrapping for a hypothesis test).

$\endgroup$
  • $\begingroup$ I supposed so... $\endgroup$ – Bakaburg Oct 14 '15 at 20:52
  • $\begingroup$ I edited the question adding a note about the bias corrected estimate. $\endgroup$ – Bakaburg Oct 14 '15 at 21:13
  • $\begingroup$ @Bakaburg if there is evidence of bias, then a bias corrected estimate would be preferred, along with some estimate of precision. $\endgroup$ – user75138 Oct 15 '15 at 3:46
  • $\begingroup$ Would you like to elaborate on this? $\endgroup$ – Bakaburg Oct 22 '15 at 12:53

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.