jackknife_resampling¶

astropy.stats.
jackknife_resampling
(data)[source] [edit on github]¶ 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: data : numpy.ndarray
Original sample (1D array) from which the jackknife resamples will be generated.
Returns: resamples : numpy.ndarray
The ith row is the ith jackknife sample, i.e., the original sample with the ith measurement deleted.
References
[R91] McIntosh, Avery. “The Jackknife Estimation Method”. <http://people.bu.edu/aimcinto/jackknife.pdf> [R92] Efron, Bradley. “The Jackknife, the Bootstrap, and other Resampling Plans”. Technical Report No. 63, Division of Biostatistics, Stanford University, December, 1980. [R93] Cowles, Kate. “Computing in Statistics: The Jackknife, Lecture 11”. <http://homepage.stat.uiowa.edu/~kcowles/s166_2009/lect11.pdf>. September, 2009.