-
Notifications
You must be signed in to change notification settings - Fork 36
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
BCA method and alpha defaults #26
Comments
@mwort "typical 5-95% CI" - I believe "95%CI" is 95% wide ie between 2.5% and 97.5%. Is there a reference that points to the other convention? |
@aizvorski It does appear that that part of Efron and Tibshirani uses
I'm reasonably sure this isn't possible. E&T are likely saying that for the more usual case where |
Hi @cgevans, thanks for this great scikit. I have questions regarding the implementation of the BCA method and the alpha argument to the
ci
function and I apologize if they might stem from an insufficient understanding of bootstrapping.The
bca
method seems to take N+n_samples function evaluation rather than just n_samples, I believe because it calculates the jackknife mean with N function calls. This gets annoying if you have many empirical observations (e.g. N ~ n_samples). Efron and Tibshirani (1994)[0] state that their implementation uses "little more effort than for the percentile intervals" (p.178). A bit of a longshot but isnt there a way of reusing the previous evaluations?And how come the percentiles default to
alpha/2, 1-alpha/2
rather than justalpha, 1-alpha
(e.g. as on E&T eq. 13.5, p.171)? Wouldn't typical 5-95% CI have an alpha of 0.05 (rather than 0.1)?[0] https://books.google.de/books?id=gLlpIUxRntoC&lpg=PR14&ots=A9DxX6J6G6&dq=efron%20tibshirani%20bootstrap&lr&pg=PA171#v=onepage&q&f=false
The text was updated successfully, but these errors were encountered: