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| انحدار هابر (Huber Regression)× | انحدار الكوانتيل× | |
|---|---|---|
| المجال≠ | الإحصاء | الاقتصاد القياسي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1964 | 1978 |
| صاحب الطريقة≠ | Peter J. Huber | Koenker & Bassett |
| النوع≠ | Robust linear regression (M-estimation) | Conditional quantile regression |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة≠ | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| ذات صلة | 5 | 5 |
| الملخص≠ | Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit. | 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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