השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| OLS לא-לינארי (ריבועים פחותים רגילים לא-לינאריים)× | שיטת הריבועים הפחותים המוכללת (GLS)× | |
|---|---|---|
| תחום≠ | אקונומטריקה | סטטיסטיקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1974–1987 | 1935 |
| הוגה השיטה≠ | Gallant (1987); Wooldridge (2010) for econometric treatment | Alexander Craig Aitken |
| סוג≠ | Nonlinear regression estimator | Linear estimator |
| מקור מכונן≠ | 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 ↗ |
| כינויים≠ | nonlinear least squares, NLS, NLLS, nonlinear regression | GLS, Aitken estimator, EGLS, feasible GLS |
| קשורות≠ | 5 | 3 |
| תקציר≠ | 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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