방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 강건 분위수 회귀× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1993–1997 | 2019 |
| 창시자≠ | Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Robust semiparametric regression | Linear regression |
| 원전≠ | Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 6 | 5 |
| 요약≠ | Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions. | 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). |
| ScholarGate데이터셋 ↗ |
|
|