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| M-Estimators (강건 회귀)× | 조건부 분위수 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2009 | 1978 |
| 창시자≠ | Peter J. Huber | Koenker & Bassett |
| 유형≠ | Robust linear regression | Conditional quantile regression |
| 원전≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 별칭≠ | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 관련 | 5 | 5 |
| 요약≠ | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to 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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