Jackknife resampling

In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap. Given a sample of size ${\displaystyle n}$, a jackknife estimator can be built by aggregating the parameter estimates from each subsample of size ${\displaystyle (n-1)}$ obtained by omitting one observation.^[1]

The jackknife technique was developed by Maurice Quenouille (1924–1973) from 1949 and refined in 1956. John Tukey expanded on the technique in 1958 and proposed the name "jackknife" because, like a physical jack-knife (a compact folding knife), it is a rough-and-ready tool that can improvise a solution for a variety of problems even though specific problems may be more efficiently solved with a purpose-designed tool.^[2]

The jackknife is a linear approximation of the bootstrap.^[2]