# The Jackknife Estimation Method

arXiv: https://arxiv.org/abs/1606.00497 · HTML: https://mlsanity.com/paper/1606.00497/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening](https://mlsanity.com/paper/2601.21590/) (2026)
- [Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective](https://mlsanity.com/paper/2310.03556/) (2023)

## Abstract (arXiv)

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 techniques, I will quickly delineate two similar resampling methods: the bootstrap and the permutation test. I then outline the Jackknife method and show an example of its use.