= Bootstrap standard error
{title2=$\widehat{\operatorname{se}}_*=(\operatorname{Var}_*\widehat\theta^*)^{1/2}$}
Replace the unknown sampling distribution by the <empirical distribution>, generate independent <bootstrap samples>, and recompute the statistic. The sample standard deviation of those recomputed values estimates the conditional bootstrap standard deviation, which approximates the statistic's sampling <standard error> when the bootstrap is consistent. It is different from the Monte Carlo standard error of the estimated bootstrap mean.
Back to article page
