Порівняння методів

	Бутстреп-інференс	Метод ковзного виключення (Jackknife Resampling)	Тест з перестановки (рандомізації)
Галузь	Статистика	Статистика	Статистика
Родина	Regression model	Regression model	Regression model
Рік появи	1979	1956	2005
Автор методу	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)	Good (2005); Edgington & Onghena (2007); resampling tradition
Тип	Resampling-based inference	Resampling / bias and variance estimation	Nonparametric resampling test
Основоположне джерело	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	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
Інші назви	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Пов'язані	5	5	5
Підсумок	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 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.