قارن الطرق

	الـمربعات الصغرى العادية غير الخطية (Nonlinear OLS)	التقدير المربعات الصغرى المعممة (GLS)
المجال	الاقتصاد القياسي	الإحصاء
العائلة	Regression model	Regression model
سنة النشأة	1974–1987	1935
صاحب الطريقة	Gallant (1987); Wooldridge (2010) for econometric treatment	Alexander Craig Aitken
النوع	Nonlinear regression estimator	Linear estimator
المصدر التأسيسي	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.
الأسماء البديلة	nonlinear least squares, NLS, NLLS, nonlinear regression	GLS, Aitken estimator, EGLS, feasible GLS
ذات صلة	5	3
الملخص	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.