: Sample extreme values are biased estimators of the end-points of a distribution, and hence, jackknifing is useful. However, the properties of jackknifing in such a case differ considerably from those in the regular case. These are studied here. Along with a modification of jackknifing, some applications are also considered. (Author)

SUMMARY Whether to jackknife a parameter estimate or some function of it has in the past relied on recommendations based on folklore rather than statistical analyses. This paper puts the jackknifed estimator into the context of finding a least squares estimator from a model involving a location parameter with additive "pseudo" errors. Obtaining and interpreting confidence intervals from the partitioning of the "total" sum of squares into "residual" sum of squares, plus a sum of squares due to a hypothesized value of the parameter, requires the error variance to be constant. This in turn can equally be interpreted as requiring the variance of the original estimator to be constant. Therefore, using this criterion of jackknifing "pivotal quantities", it is possible in principle to find a prejackknifing transformation for any general problem.

On Jackknifing in Estimating The Finite End–Points of a Distribution*

Transformations and the Jackknife

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA058963&Location=U2&doc=GetTRDoc.pdf

On Jackknifing in Estimating The Finite End–Points of a Distribution*

Citations

Transformations and the Jackknife

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