方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 非线性OLS（非线性最小二乘法）× | 广义最小二乘法 (GLS)× | |
|---|---|---|
| 领域≠ | 计量经济学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1974–1987 | 1935 |
| 提出者≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Alexander Craig Aitken |
| 类型≠ | Nonlinear regression estimator | Linear estimator |
| 开创性文献≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| 别名≠ | nonlinear least squares, NLS, NLLS, nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| 相关≠ | 5 | 3 |
| 摘要≠ | Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal. | 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. |
| ScholarGate数据集 ↗ |
|
|