Joseph `'Jacknife'` Megessey
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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 .
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JACKKNIFE definition: 1. If a truck that has two parts jackknifes, one part moves around so far towards the other part…. Learn more.
Learn about the jackknife method, a resampling technique that provides estimates of bias and standard error of an estimate. Find chapters and articles on jackknife applications, variance estimation, and comparison with bootstrap.
The jackknife method was developed by Quenouille (1949, 1956) and John Wilder Tukey (1958). It is now the most widely used method thanks to computers, which can generate a large amount of data in a very short time.
It could be a scalar parameter of the GWN model (e.g. μμ or σσ) or it could be a scalar function of the parameters of the GWN model (e.g. VaR or SR). Let ˆθ^θ denote the plug-in estimator of θθ. The goal is to compute a numerical estimated standard error for ˆθ^θ, ^ se(ˆθ)ˆse(^θ), using the jackknife resampling method.
1 Jan 2012 · The jackknife is a general technique to remove bias and estimate variance of estimators based on iid samples. It involves computing leave-one-out estimators and pseudo-values, and averaging them in a specific way.
