방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 비선형 가중 최소제곱법 (NWLS)× | 일반화 최소제곱법 (GLS)× | |
|---|---|---|
| 분야≠ | 계량경제학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1960s–1980s (formalized in applied econometrics) | 1935 |
| 창시자≠ | Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting | Alexander Craig Aitken |
| 유형≠ | Nonlinear regression estimator | Linear estimator |
| 원전≠ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education. ISBN: 978-0134461366 | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| 별칭≠ | NWLS, nonlinear weighted least squares, weighted nonlinear regression, heteroscedasticity-corrected nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| 관련 | 3 | 3 |
| 요약≠ | Nonlinear Weighted Least Squares combines the flexibility of nonlinear regression with the variance-stabilizing power of observation-level weights. It minimises a weighted sum of squared residuals around a user-specified nonlinear mean function, making it the method of choice when the relationship is inherently nonlinear and error variance differs across observations. | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. |
| ScholarGate데이터셋 ↗ |
|
|