Võrdle meetodeid

	Topelt (iteratiivne) bootstrap	Plokk-bootstrapping (liikuv plokk ja statsionaarne)	Permutatsioonitest (randomiseerimistest)
Valdkond	Statistika	Statistika	Statistika
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1986	1989	2005
Looja	Hall (1986); Beran (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Good (2005); Edgington & Onghena (2007); resampling tradition
Tüüp	Resampling calibration (nested bootstrap)	Resampling inference for dependent data	Nonparametric resampling test
Algallikas	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Rööpnimetused	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Seotud	5	5	5
Kokkuvõte	The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers.	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 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.