Methoden vergleichen
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| Bayesian Bootstrap (Rubin)× | Block-Bootstrap (Moving Block und Stationär)× | Methode der kleinsten Quadrate (OLS)× | |
|---|---|---|---|
| Fachgebiet≠ | Statistik | Statistik | Ökonometrie |
| Familie | Regression model | Regression model | Regression model |
| Entstehungsjahr≠ | 1981 | 1989 | 2019 |
| Urheber≠ | Rubin (1981); large-sample theory by Lo (1987) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Resampling / posterior simulation | Resampling inference for dependent data | Linear regression |
| Wegweisende Quelle≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasnamen≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwandt | 5 | 5 | 5 |
| Zusammenfassung≠ | The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated. | 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). |
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