Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Блоков бутстрап (подвижни блокове и стационарен)× | Метод на най-малките квадрати (МНК)× | Тест с пермутации (рандомизация)× | |
|---|---|---|---|
| Област≠ | Статистика | Иконометрия | Статистика |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1989 | 2019 | 2005 |
| Създател≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Wooldridge (textbook treatment); classical least squares | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Тип≠ | Resampling inference for dependent data | Linear regression | Nonparametric resampling test |
| Основополагащ източник≠ | 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 | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Други названия≠ | 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 |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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