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In statistics, the jackknife 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 , the jackknife estimate is found by aggregating the estimates of each estimate in the sample. The jackknife technique was developed by Maurice Quenouille (1949, 1956). John Tukey (1958) expanded on the technique and proposed the name "jackknife" since, like a Boy Scout's jackknife, it is a "rough and ready" tool that can solve 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. ==Estimation== The jackknife estimate of a parameter can be found by estimating the parameter for each subsample omitting the ''i''th observation to estimate the previously unknown value of a parameter (say ). : 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Jackknife resampling」の詳細全文を読む スポンサード リンク
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