Use resampling techniques to estimate descriptive statistics and confidence intervals from sample data when parametric test assumptions are not met, or for small samples from non-normal distributions. Bootstrap methods choose random samples with replacement from the sample data to estimate confidence intervals for parameters of interest. Jackknife systematically recalculates the parameter of interest using a subset of the sample data, leaving one observation out of the subset each time (leave-one-out resampling). From these calculations, it estimates the parameter of interest for the entire data sample. If you have a Parallel Computing Toolbox™ license, you can use parallel computing to speed up resampling calculations.
Use bootstrap and jackknife methods to measure the uncertainty in the estimated parameters and statistics.
Get started with parallel statistical computing.
Speed up the jackknife using parallel computing.
Speed up cross-validation using parallel computing.
Speed up the bootstrap using parallel computing.