Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| OLS neliniar (Cea mai mică sumă a pătratelor reziduurilor neliniară)× | Metoda celor mai mici pătrate generalizate neliniare (NGLS)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1974–1987 | 1975 |
| Autorul original≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Gallant (1975); extended by Davidson & MacKinnon |
| Tip≠ | Nonlinear regression estimator | Nonlinear estimator |
| Sursa seminală≠ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 | Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600 |
| Denumiri alternative | nonlinear least squares, NLS, NLLS, nonlinear regression | NGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLS |
| Înrudite≠ | 5 | 2 |
| Rezumat≠ | 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. |
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