Bootstrap method is a way of simulating results for a larger number of samples based on the given sample. The bootstrap method is used to quantify the uncertainty in any parameter estimate (e.g., mean, variance, percentile value, etc.). All bootstrap methods involve generating hypothetical samples from the original sample. Each hypothetical sample is called a Bootstrap Sample.
Bootstrap method has the following assumptions:
- The sample taken should be a valid representative of the populations.
- Bootstrap method takes sampling with replacement from the sample. Each sub sampling is independent and identical distribution (i.i.d.). In other words, it assumes that the sub samples come from the same distribution of the population, but each sample is drawn independently from the other samples.
- The bootstrap works by computing the desired statistic for a sub sample of the data set. The sub sampling is done with replacement and the size of the sample is equal to the size of the original sample. The desired statistic is calculated for each sub sample. The collection of these statistics is used as an estimate of the sampling distribution.