Strength and Weakness of Bootstrap Sampling
- (Negative) Bootstrap method is not exact. For large sample, permutation test perform better than bootstrap.
- (Positive) Bootstrap requires very minimum assumption. Even if permutation test fail, bootstrap method still can do.
- (Positive) Though it can be used for parametric method (i.e. distribution is known), bootstrap method is most useful when the sample distribution is unknown (non-parametric).
Note: Permutation test is similar to bootstrap that it resample from the sample but not randomly. Instead, it considers all possible permutation of the sample.
Rate & comment of this tutorial
This tutorial is copyrighted .
Preferable reference for this tutorial is
Teknomo, Kardi. Bootstrap Sampling Tutorial. http://people.revoledu.com/kardi/tutorial/Bootstrap/