JACKKNIFE: a general-purpose package for generating multivariate jackknife analyses

Jackknife Resampling

The Jackknife Estimation Method

Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for parametric estimation known as the Jackknife procedure. To outline the usefulness of the method and its place in the general class of statistical resampling techniq...

The Jackknife in Classification

Breiman (1996), in an important contribution to the eld of classi cation, introduced the notion of resampling for improving classi cation rules. Freund and Schapire (1996) developed an other algorithm that exploits the resampling. This paper presents a Jackknife-type approach combined with the Freund and Schapire (FS) method. We propose and explore some alternatives to this algorithm and nd tha...

Jackknife Hervé Abdi ⋅

The jackknife or “leave one out” procedure is a cross-validation technique first developed by Quenouille to estimate the bias of an estimator. John Tukey then expanded the use of the jackknife to include variance estimation and tailored the name of jackknife because like a jackknife—a pocket knife akin to a Swiss army knife and typically used by boy scouts—this technique can be used as a “quick...

Confidence intervals for random forests: the jackknife and the infinitesimal jackknife

We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging proposed by Efron (1992, 2013) that are based on the jackknife and the infinitesimal jackknife (IJ). In practice, bagged predictors are computed using a finite number B of bootstrap replicates, and worki...