User:0.25cm/Bootstrapping (statistics)
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Disadvantages[edit]
Bootstrapping depends heavily on the estimator used and, though simple, ignorant use of bootstrapping will not always yield asymptotically valid results and can lead to inconsistency. Although bootstrapping is (under some conditions) asymptotically consistent, it does not provide general finite-sample guarantees. The result may depend on the representative sample. The apparent simplicity may conceal the fact that important assumptions are being made when undertaking the bootstrap analysis (e.g. independence of samples or large enough sample size) where these would be more formally stated in other approaches.
Also, bootstrapping can be time-consuming.
Regression[edit]
In regression problems, case resampling refers to the simple scheme of resampling individual cases – often rows of a data set. For regression problems, as long as the data set is fairly large, this simple scheme is often acceptable. However, the method is open to criticism[citation needed].
In regression problems, the explanatory variables are often fixed, or at least observed with more control than the response variable. Also, the range of the explanatory variables defines the information available from them. Therefore, to resample cases means that each bootstrap sample will lose some information. As such, alternative bootstrap procedures should be considered.