[PDF] an introduction to the bootstrap

What is an introduction to the bootstrap method?

The Bootstrap Sampling Method
Generally, bootstrap involves the following steps: A sample from population with sample size n. Draw a sample from the original sample data with replacement with size n, and replicate B times, each re-sampled sample is called a Bootstrap Sample, and there will totally B Bootstrap Samples.

How does bootstrapping work?

Bootstrapping is done by repeatedly sampling (with replacement) the sample dataset to create many simulated samples.
Each simulated bootstrap sample is used to calculate an estimate of the parameter, and these estimates are then combined to form a sampling distribution.

When to use bootstrap?

When to Use Bootstrap

When to use bootstrap?

Bootstrapping is a type of resampling where large numbers of smaller samples of the same size are repeatedly drawn, with replacement, from a single original sample.
For example, let's say your sample was made up of ten numbers: 49, 34, 21, 18, 10, 8, 6, 5, 2, 1.
You randomly draw three numbers 5, 1, and 49.