bootstrap standard error confidence interval

An extremely useful application of the bootstrap is the construction of confidence intervals Recall that a (1 − α) confidence interval for θ, computed over z1, zn, is a random interval [L, U] satisfying P(L ≤ θ ≤ U)=1 − α

For example: the median, other percentiles or the trimmed mean These are statistics where, even for normal distributions, it can be difficult to compute a 

4 jan 2017 · If an exact CI can be formed (e g , sample mean), bootstrap CI should closely t Confidence Interval with Bootstrap Standard Error Uses the 

23 déc 2012 · The standard error of a statistic can be estimated using the standard deviation of the bootstrap distribution Based on this sample, give a 95 confidence interval for the true proportion of Reese's Pieces that are orange

This is called the ideal bootstrap estimate of the standard error of s(x) Notice that for the In the normal case, what are the confidence intervals for? Remember 

statistic (here, the mean)—that is, to get a bootstrap standard error—100 or 200 bootstrap samples should be more than sufficient To find a confidence interval, 

sufficiently large samples), and uses the bootstrap estimate of sampling variance, and perhaps of bias, to construct a 100(1 − α)-percent confidence interval of 

constructed by sampling with replacement from the data vector y , the so-called non-parametric bootstrap, or by sampling from the distribution function