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| 강건 가중 최소제곱법 (Robust WLS)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1964/1981 | 2019 |
| 창시자≠ | Huber, P. J. | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Robust weighted regression | Linear regression |
| 원전≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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