Bootstrapping, and Other Resampling Methods

11 Aug 2021 11:48

Bootstrapping is a way of figuring out the properties of statistical estimators (and other procedures, like hypothesis tests) by simulation. What we would really like to know his how different our answers could have been, if we re-ran our experiment. We can't actually do this, but we can fit a model to our data and simulate from it, and see what answer we'd get from the simulations. We can even do this from exceedingly general non-parametric estimates, like re-sampling the original data. This is a brilliant idea, and my default way of handling the uncertainty of estimation in complex models or with complex systems. But having just written 3500 words on this for a magazine, I feel absolutely no inclination to explain myself further.

I most interested in resampling techniques for dependent data, and would be ecstatic if I could figure out a non-parametric bootstrap for networks. — Presumably universal prediction algorithms could be used for this purpose?

See also: Cross-Validation; Nonparametric Confidence Sets for Functions; Statistics