So sánh phương pháp

	Bootstrap Khối (Khối Di động và Tĩnh)	Phương pháp lấy mẫu lại Jackknife	Hồi quy Bình phương Tối thiểu Thông thường (OLS)	Kiểm định hoán vị (Ngẫu nhiên hóa)
Lĩnh vực	Thống kê	Thống kê	Kinh tế lượng	Thống kê
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	1989	1956	2019	2005
Người khởi xướng	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
Loại	Resampling inference for dependent data	Resampling / bias and variance estimation	Linear regression	Nonparametric resampling test
Công trình gốc	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Tên gọi khác	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Liên quan	5	5	5	5
Tóm tắt	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.