Сравнение методов

	Блочная бутстрэп-выборка (скользящий блок и стационарная)	Бутстреп-вывод	Тест перестановок (рандомизация)
Область	Статистика	Статистика	Статистика
Семейство	Regression model	Regression model	Regression model
Год появления	1989	1979	2005
Автор метода	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Тип	Resampling inference for dependent data	Resampling-based inference	Nonparametric resampling test
Основополагающий источник	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Другие названия	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Связанные	5	5	5
Сводка	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).	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	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.