jackknife_resampling¶

astropy.stats.
jackknife_resampling
(data)[source]¶ Performs jackknife resampling on numpy arrays.
Jackknife resampling is a technique to generate ‘n’ deterministic samples of size ‘n1’ from a measured sample of size ‘n’. Basically, the ith sample, (1<=i<=n), is generated by means of removing the ith measurement of the original sample. Like the bootstrap resampling, this statistical technique finds applications in estimating variance, bias, and confidence intervals.
 Parameters
 datanumpy.ndarray
Original sample (1D array) from which the jackknife resamples will be generated.
 Returns
 resamplesnumpy.ndarray
The ith row is the ith jackknife sample, i.e., the original sample with the ith measurement deleted.
References
 1
McIntosh, Avery. “The Jackknife Estimation Method”. <http://people.bu.edu/aimcinto/jackknife.pdf>
 2
Efron, Bradley. “The Jackknife, the Bootstrap, and other Resampling Plans”. Technical Report No. 63, Division of Biostatistics, Stanford University, December, 1980.
 3
Jackknife resampling <https://en.wikipedia.org/wiki/Jackknife_resampling>