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 (GLS)× | |
|---|---|---|
| Domeniu≠ | Econometrie | Statistică |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1974–1987 | 1935 |
| Autorul original≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Alexander Craig Aitken |
| Tip≠ | Nonlinear regression estimator | Linear estimator |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | nonlinear least squares, NLS, NLLS, nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| Înrudite≠ | 5 | 3 |
| 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. | 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. |
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