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| 강건 가중 최소제곱법 (Robust WLS)× | 조건부 분위수 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1964/1981 | 1978 |
| 창시자≠ | Huber, P. J. | Koenker & Bassett |
| 유형≠ | Robust weighted regression | Conditional quantile regression |
| 원전≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 별칭≠ | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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