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| Робустна регресия× | Квантилна регресия× | |
|---|---|---|
| Област≠ | Статистика | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1964 | 1978 |
| Създател≠ | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) | Koenker & Bassett |
| Тип≠ | Regression with outlier resistance | Conditional quantile regression |
| Основополагащ източник≠ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Други названия≠ | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Свързани≠ | 6 | 5 |
| Резюме≠ | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. | 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. |
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