Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластерно-стійкі стандартні похибки× | Дикий бутстреп для регресійних висновків× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи | 1986 | 1986 |
| Автор методу≠ | Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis) | Wu (1986); refined by Davidson & Flachaire (2008) |
| Тип≠ | Robust variance estimation for regression | Resampling-based regression inference |
| Основоположне джерело≠ | Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22. DOI ↗ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ |
| Інші назви | clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small. | The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered. |
| ScholarGateНабір даних ↗ |
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