Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастный взвешенный метод наименьших квадратов (Robust WLS)× | Квантильная регрессия× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1964/1981 | 1978 |
| Автор метода≠ | Huber, P. J. | Koenker & Bassett |
| Тип≠ | Robust weighted regression | Conditional quantile regression |
| Основополагающий источник≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Другие названия≠ | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Связанные | 5 | 5 |
| Сводка≠ | Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateНабор данных ↗ |
|
|