A simulation study of balanced and antithetic bootstrap resampling methods

An extensive simulation study is conducted to compare the performance between balanced and antithetic resampling for the bootstrap in estimation of bias, variance, and percentiles when the statistic of interest is the median, the square root of the absolute value of the mean, or the median absolute deviations from the median.Simulation results reveal that balanced resampling provide better efficiencies in most cases; however, antithetic resampling is superior in estimating bias of the median. We also investigate the possibility of combining an existing efficient bootstrap computation of Efron (1990) with balanced or antithetic resampling for percentile estimation. Results indicate that the combination method does indeed offer gains in performance though the gains are much more dramatic for the bootstrap t statistic thanfor any of the three statistics of interest as describedabove.