26: Resampling methods (bootstrapping)

Показать описание

Bootstrapping to estimate parameters (e.g., confidence intervals) for single samples. Balanced bootstrapping for inherent biased parameters.

Рекомендации по теме

Комментарии

the only good straight foward, video on bootstrapping out there.
No book-canned stratified answer, as it is so often common in statistics.
Thank you, this video is a piece of art.

marciofernandes

I learned more in this 10 minute video than I did in my 3 hour lecture.

ltbd

You're a hero. This video taught me more about bootstrapping than several hours of lectures.

dunslax

You do a good job at explaining this. I never thought of plotting the sample means from 1to 10000 or more in R.

yaweli

Matthew, this is very nice video with clear elucidation of bootstrapping. Thanks you for sharing.

drpindoria

Great and concise explanation, thank you! Just what I needed to understand what my prof. wanted me to do and why!

Titolius

Well explained in a simple way. Thank you!

merumomo

Thanks, nice video of a very useful series. Just a doubt : at the end you say that a way to correct the biased estimation of the variance is to add a quantity to each value. But this does not change the variance ... Could you elaborate on the last part of the video about balanced bootstrap?

ferdinandoinsalata

Great presentation. I thought you were going to construct 95% CI for R2.

SNPolka

Great presentation. One thing that’s bothering me is that the 95% CI is constructed so that the CIs 95% of the time contain the true parameter value. As said on one slide. The next slide shows 95% of sample means not of CIs. I imagine this holds true but it is not addressed. Would be good to get confirmation.

sassora

about the balancing part: we compute the bootstrap mean, then we subtract the difference between bootstrap mean and sample mean and get... sample mean. why not use sample mean from the beginning?

getupanddance

First off, excellent vid. My question is - and I hope I state it clearly: Is balancing the bootstrap necessary? Can't it be assumed that an obvious outlier in a small data set is an anomaly, and the fact that the resampling doesn't pick it up as often means that it is "correcting" the data?

charliekrajewski

re adjusing a BS parameter to counter bias, a question arises. Why BS if you are going to end up with same adjusted parameter value as the observed value by adding back the difference between the obs sample's paraemter g variance eg say var_obs =0.15 and the bs parameter eg variance var_bs=0.1. Adding back the difference will simply adjust the bs value to the sample parameter value.

daducky