Porovnat metody

	Odhad pomocí jackknife metody	Simulace Monte Carlo	Permutační (randomizační) test
Obor	Statistika	Rozhodování	Statistika
Rodina	Hypothesis test	MCDM	Regression model
Rok vzniku	1956	1949	2005
Tvůrce	Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)	Metropolis, N., Ulam, S.	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Bias and variance estimation	Robustness wrapper — Monte Carlo uncertainty propagation	Nonparametric resampling test
Původní zdroj	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Další názvy	delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme	—	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Příbuzné	3	0	5
Shrnutí	Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible 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.