Predictive inference with the jackknife

TLDR: In this article, the authors introduce the jackknife+ method for constructing predictive confidence intervals, which is based on the leave-one-out predictions at the test point to account for the variability in the fitted regression function Assuming exchangeable training samples, this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically.

Abstract: This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability Further, we extend the jackknife+ to $K$-fold cross validation and similarly establish rigorous coverage properties Our methods are related to cross-conformal prediction proposed by Vovk (Ann Math Artif Intell 74 (2015) 9–28) and we discuss connections