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Resampling Techniques

Resample data set using bootstrap, jackknife, and cross validation

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.


bootciBootstrap confidence interval
bootstrpBootstrap sampling
crossvalEstimate loss using cross-validation
datasampleRandomly sample from data, with or without replacement
jackknifeJackknife sampling
randsampleRandom sample


Resampling Statistics

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.