مقایسهٔ روش‌ها

	بস্পতি بوت استرپ (بلوک متحرک و ایستای)	استنتاج بوتسترپ	آزمون جایگشتی (تصادفی‌سازی)
حوزه	آمار	آمار	آمار
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1989	1979	2005
پدیدآور	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
نوع	Resampling inference for dependent data	Resampling-based inference	Nonparametric resampling test
منبع بنیادین	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
نام‌های دیگر	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
مرتبط	5	5	5
خلاصه	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.