# 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 ‘n-1’ from a measured sample of size ‘n’. Basically, the i-th sample, (1<=i<=n), is generated by means of removing the i-th 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 (1-D array) from which the jackknife resamples will be generated. resamples : numpy.ndarray The i-th row is the i-th jackknife sample, i.e., the original sample with the i-th measurement deleted.

References

 [R52] McIntosh, Avery. “The Jackknife Estimation Method”.
 [R53] Efron, Bradley. “The Jackknife, the Bootstrap, and other Resampling Plans”. Technical Report No. 63, Division of Biostatistics, Stanford University, December, 1980.
 [R54] Cowles, Kate. “Computing in Statistics: The Jackknife, Lecture 11”. . September, 2009.