Confronta i metodi

	Bootstrap a Blocchi (Blocco Mobile e Stazionario)	Ricalcolo del Jackknife	Regression with Ordinary Least Squares (OLS)
Campo	Statistica	Statistica	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1989	1956	2019
Ideatore	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares
Tipo	Resampling inference for dependent data	Resampling / bias and variance estimation	Linear regression
Fonte seminale	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
Alias	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
Correlati	5	5	5
Sintesi	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).