Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Epälineaarinen pienimmän neliösumman menetelmä (Nonlinear Least Squares, NLS)× | Yleistetty pienimmän neliösumman menetelmä (GLS)× | |
|---|---|---|
| Tieteenala≠ | Ekonometria | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1974–1987 | 1935 |
| Kehittäjä≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Alexander Craig Aitken |
| Tyyppi≠ | Nonlinear regression estimator | Linear estimator |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | nonlinear least squares, NLS, NLLS, nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | 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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