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| Block Bootstrap (Moving Block och Stationary)× | Bootstrapinferens× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|---|
| Ämnesområde≠ | Statistik | Statistik | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1989 | 1979 | 2019 |
| Upphovsperson≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Bradley Efron | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Resampling inference for dependent data | Resampling-based inference | Linear regression |
| Ursprungskälla≠ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | 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) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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). | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | 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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