Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная линейная регрессия× | Квантильная регрессия× | |
|---|---|---|
| Область≠ | Машинное обучение | Эконометрика |
| Семейство≠ | Machine learning | Regression model |
| Год появления≠ | 1964–1987 | 1978 |
| Автор метода≠ | Huber, P. J.; Rousseeuw, P. J. | Koenker & Bassett |
| Тип≠ | Outlier-resistant supervised regression | Conditional quantile regression |
| Основополагающий источник≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Другие названия≠ | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Связанные | 5 | 5 |
| Сводка≠ | Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation. | 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 ↗ |
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