Resampling your own data to estimate how good your point-estimator is, and to reduce its bias. This is AFAICT a frequentist technique without an immediate Bayesian interpretation. 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.
Bootstrap bias correction
As opp variance estimation. 🏗
Bootstrap for dependent data
e.g., as presaged, time series. Parametric bootstrap would be the logical default choice, right?
As a Bayesian method
See Lyddon, Holmes, and Walker (2019).
Pedagogic
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