terms, the jackknife is a method
of determining the behaviour of a model
or a distribution
. It is one of the Monte Carlo
methods of simulation, along with permutation
Using a real data series, jackknifing allows a researcher to create a large number of hypothetical cases by resampling the data by eliminating one (or more) case at a time. It is used principally to determine the effect outliers or abberrant cases have on the results of the study. The fundamental procedure is as follows:
- Identify the data series of interest. This data series will have n observations.
- Calculate the parameter(s) of interest on the original data.
- Eliminate the 1st observation from the data series.
- Calculate the parameter(s) of interest on the reduced data set.
- Repeat steps 3-4 for the 2nd to nth observation.
- If desired, repeat steps 3-5, eliminating more than one case at a time.
This method of simulation is particularly useful when the data being studied do not conform to typical distributions, or may contain a lot of uncertainty
tells me that this is called leave-one-out cross-validation
by computer scientists
, but that the procedure is not quite. Leave-one-out cross validation is used to estimate the error probabilites, while jackknifing can be used to estimate many different parameters or even distributions.