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 , a jackknife estimator can be built by aggregating the parameter estimates from each subsample of size 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]
^Efron 1982, p. 2.
^ abCameron & Trivedi 2005, p. 375.
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