Statistical Consultants Ltd



Simulation Techniques
, Statistical Analysis Techniques

Like Monte Carlo methods, the bootstrap is a computationally intensive method but differs in that it doesn’t rely on an assumed data generation process.  Instead it relies on taking samples from an existing data set.  It involves the following steps:
  1. Generate a random sample (with replacement) from the original data set.  The sample should be the same size as the original data set.
  2. Calculate the values for the test statistics or estimators using this randomly generated sample, and store the results.
  3. Repeat steps 1 and 2 many times (often thousands of times).
  4. Evaluate the performance of the estimator or the accuracy of the test statistic based on the set of stored results e.g. find the mean and standard error of the stored results.

See also:

Monte Carlo Methods


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