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?