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）× | 非线性广义最小二乘 (NGLS)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1974–1987 | 1975 |
| 提出者≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Gallant (1975); extended by Davidson & MacKinnon |
| 类型≠ | Nonlinear regression estimator | Nonlinear estimator |
| 开创性文献≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 | Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600 |
| 别名 | nonlinear least squares, NLS, NLLS, nonlinear regression | NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS |
| 相关≠ | 5 | 2 |
| 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. | Nonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates. |
| ScholarGate数据集 ↗ |
|
|