26: Resampling methods (bootstrapping)

Well explained in a simple way. Thank you!

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.

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?

THANK YOU!!

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?

if we want the resampling mean value to be greater than then how to proceed

Please, some material about gibbs sampling? I need it so much.

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

Thank you so much for this video.

how to do bootstrapping with gretl please?

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?

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

this is so helpful, thank you !

what does resampling the data with replacement means??

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.

ty pham

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.

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

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

Sound quality is bad!!

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

You never added why you would want to do balanced bootstrapping. It is to get better performance statistics.

honoured to be the 1000 one click like

100,000th viewer! Thank you

wow you are a lifesaver

Thank you for the explanation!

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

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