Strength and Weakness of Bootstrap Sampling

By Kardi Teknomo, PhD.

<Index | Previous | Next >

• (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.

<Index | Previous | Next >

Give your comment on this tutorial

This tutorial is copyrighted.

Preferable reference for this tutorial is

Teknomo, Kardi. Bootstrap Sampling Tutorial. http://people.revoledu.com/kard/ tutorial/bootstrap/