Salīdzināt metodes

	Bloku robusta (kustīgo bloku un stacionārais)	Džeknaifa atkārtotā izlases metode	Permutācijas (randomizācijas) tests
Nozare	Statistika	Statistika	Statistika
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1989	1956	2005
Autors	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Quenouille (1956); reviewed by Miller (1974)	Good (2005); Edgington & Onghena (2007); resampling tradition
Tips	Resampling inference for dependent data	Resampling / bias and variance estimation	Nonparametric resampling test
Pirmavots	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Citi nosaukumi	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Saistītās	5	5	5
Kopsavilkums	Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).	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.	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.