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.
bootci | Bootstrap confidence interval |
bootstrp | Bootstrap sampling |
combnk | Enumeration of combinations |
crossval | Loss estimate using cross-validation |
datasample | Randomly sample from data, with or without replacement |
jackknife | Jackknife sampling |
randsample | Random sample |
Use bootstrap and jackknife methods to measure the uncertainty in the estimated parameters and statistics.
Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
Get started with parallel statistical computing.
Implement Jackknife Using Parallel Computing
Speed up the jackknife using parallel computing.
Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
Implement Bootstrap Using Parallel Computing
Speed up the bootstrap using parallel computing.