Σύγκριση μεθόδων

	Block Bootstrap (Moving Block και Stationary)	Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)	Δοκιμή Μετάθεσης (Τυχαιοποίησης)
Πεδίο	Στατιστική	Οικονομετρία	Στατιστική
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1989	2019	2005
Δημιουργός	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
Τύπος	Resampling inference for dependent data	Linear regression	Nonparametric resampling test
Θεμελιώδης πηγή	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	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
Εναλλακτικές ονομασίες	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Συναφείς	5	5	5
Σύνοψη	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).	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.