Salīdzināt metodes

	Džeknaifa atkārtotā izlases metode	Median Absolute Deviation (MAD) novērtējums	Permutācijas (randomizācijas) tests
Nozare	Statistika	Statistika	Statistika
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1956	1974	2005
Autors	Quenouille (1956); reviewed by Miller (1974)	Hampel (influence-curve treatment); classical robust statistics	Good (2005); Edgington & Onghena (2007); resampling tradition
Tips	Resampling / bias and variance estimation	Robust scale estimator	Nonparametric resampling test
Pirmavots	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Citi nosaukumi	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Saistītās	5	5	5
Kopsavilkums	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.