Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нелинейные взвешенные наименьшие квадраты (NWLS)× | Обобщенный метод наименьших квадратов (ОМНК)× | |
|---|---|---|
| Область≠ | Эконометрика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1960s–1980s (formalized in applied econometrics) | 1935 |
| Автор метода≠ | Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting | Alexander Craig Aitken |
| Тип≠ | Nonlinear regression estimator | Linear estimator |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | NWLS, nonlinear weighted least squares, weighted nonlinear regression, heteroscedasticity-corrected nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| Связанные | 3 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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