Jackknife Resampling (Jacknifing) in statistics is a resampling technique especially useful for variance and bias estimation. The jackknife predates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then finding the average of these calculations. Given a sample of size N, the jackknife estimate is found by aggregating the estimates of each N-1 -sized sub-sample. The proposed name “jackknife” aimed to reflect that, like a physical jack-knife (a compact folding knife), this technique 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. The jackknife is a linear approximation of the bootstrap. The jackknife estimate of a parameter can be found by estimating the parameter for each subsample omitting the ith observation to estimate the previously unknown value of a parameter. The jackknife technique can be used to estimate the bias of an estimator calculated over the entire sample.
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