Shuffling reality to produce your data

Resampling your own data to estimate how good your point-estimator is, and to reduce its bias. In general an intuitive technique. However, gets tricky for e.g. dependent data. For a handy crib sheet for bootstrap failure modes, see Thomas Lumley, When the bootstrap doesn’t work.

In the classical mode, this is a frequentist technique without an immediate Bayesian interpretation.

Commonly credited as being invented by B. Efron (1979) and theoretically justified by Gine and Zinn (1990).

Bootstrap bias correction

As opp variance estimation. NBD; Bootstrap is notionally telling you the sampling distribution. πŸ—

Bootstrap for dependent data

e.g., as presaged, time series. Parametric bootstrap would be the logical default choice, right? When does that work?

Causal bootstrap

Now a thing! (Imbens and Menzel 2021)

As a Bayesian method

There is absolutely a Bayesian bootstrap if you think hard enough about it, it turns out. Several, really. Rubin (1981) derived a Bayesian version. See Lyddon, Holmes, and Walker (2019) for a modern update, and Rasmus BΓ₯Γ₯th for a diagrammed explanation of the points of contact with frequentist bootstrap and some other things.



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