Methoden vergleichen

	Block-Bootstrap (Moving Block und Stationär)	Jackknife-Resampling	Methode der kleinsten Quadrate (OLS)
Fachgebiet	Statistik	Statistik	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1989	1956	2019
Urheber	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares
Typ	Resampling inference for dependent data	Resampling / bias and variance estimation	Linear regression
Wegweisende Quelle	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
Aliasnamen	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
Verwandt	5	5	5
Zusammenfassung	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).