: Using this distribution to estimate standard errors and construct confidence intervals . 3. Variations of the Bootstrap
: Computing the statistic of interest (e.g., mean, median, regression coefficient) for each bootstrap sample. Bootstrap methods and their application
: Drawing random samples of the same size as the original dataset with replacement. : Using this distribution to estimate standard errors
The bootstrap is a computer-intensive resampling technique first introduced by in 1979. It allows for the estimation of a statistic's sampling distribution by repeatedly sampling from the observed data with replacement . This "pulling oneself up by one's own bootstraps" approach is particularly valuable when traditional parametric assumptions (like normality) are invalid or when the theoretical distribution of a statistic is too complex to derive analytically. 2. Core Methodology The standard bootstrap procedure involves: : Drawing random samples of the same size