Jackknife

Summary

The jackknife is a resampling method that recomputes a statistic on each of the $n$ samples obtained by leaving out one observation. From these leave-one-out values it estimates the bias and the variance of the statistic and gives a bias-corrected estimate. Quenouille introduced it for bias reduction, and Tukey proposed its use for variance estimation. It works well for smooth statistics, and it is a forerunner of the bootstrap.