The Jackknife Method

Konstantin M. Zuev ()
Additional contact information
Konstantin M. Zuev: California Institute of Technology, Department of Computing and Mathematical Sciences

Chapter 5 in Fundamentals of Statistical Inference, 2026, pp 99-114 from Springer

Abstract: Abstract In the previous chapter, we have learned how to estimate a parameter of interest nonparametrically using the plug-in principle. We have also seen that a plug-in estimate may be biased: for example, the plug-in estimate of the variance is biased. In this chapter, we will learn how to reduce the bias of a biased estimate using the so-called jackknife method. The jackknife method was originally proposed by Maurice Quenouille, a British statistician of French ancestry, as a nonparametric method for estimating the bias of an estimate. In 1958, John Tukey extended the method by showing how to use it not only for estimating, but also for reducing the bias, and coined the name “jackknife.” As a pocket knife, this technique can be used as a “quick and dirty” tool for solving a variety of statistical problems.

Keywords: jackknife method; resampling; bias (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-032-03848-7_5

Ordering information: This item can be ordered from
http://www.springer.com/9783032038487

DOI: 10.1007/978-3-032-03848-7_5

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().