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A distribution-free (non-parametric) quantile estimator that is the median of a set of quantiles calculated by re-sampling the original sample a large number of times and computing a quantile for each sample.
The bootstrap quantile (Linnet, 2000)as providing the lowest root-mean-squared-error (an estimate of the bias and precision in the estimate) for both normal and skewed distributions. Another advantage is that confidence intervals can be computed for smaller sample sizes, although Linnet still recommends a sample size of at least 100.