You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The jackknife-after-bootstrap method, as described in Efron and Tibshirani's book, is a clever way to compute an uncertainty for a bootstrap estimate, without computing additional replicas. It needs a bit of additional book-keeping, so it does not come for free, but it is a vast improvement over doing a full jackknife after the bootstrap.
We could add this an keyword option in resample.bootstrap.bootstrap, or have a separate resample.bootstrap.jackknife_after_bootstrap function. I am leaning slightly towards the latter.
The text was updated successfully, but these errors were encountered:
If we put this functionality in a separate function resample.bootstrap.jackknife_after_bootstrap, then we can add this at any time.
However, we also need to consider a possible breaking change in the function confidence_interval, which should use this to produce an uncertainty for the interval. A confidence interval without an uncertainty, as we have now, is not really useful.
The jackknife-after-bootstrap method, as described in Efron and Tibshirani's book, is a clever way to compute an uncertainty for a bootstrap estimate, without computing additional replicas. It needs a bit of additional book-keeping, so it does not come for free, but it is a vast improvement over doing a full jackknife after the bootstrap.
We could add this an keyword option in
resample.bootstrap.bootstrap
, or have a separateresample.bootstrap.jackknife_after_bootstrap
function. I am leaning slightly towards the latter.The text was updated successfully, but these errors were encountered: