Bootstrap and Resampling Methods for Textile Statistics
topic
Bootstrap resampling generates a large number of simulated datasets by randomly sampling with replacement from the original textile test dataset, calculating the statistic of interest from each bootstrap sample to build an empirical sampling distribution that estimates standard error and confidence intervals without requiring parametric distribution assumptions or large sample approximations.
Role
Provides distribution-free confidence interval estimation for complex textile statistics including Weibull shape parameters, process capability indices, and composite performance indices where the analytical sampling distribution is unknown or the sample size is too small for asymptotic approximations to be reliable.