Compară metode

	OLS neliniar (Cea mai mică sumă a pătratelor reziduurilor neliniară)	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	1974–1987	2019
Autorul original	Gallant (1987); Wooldridge (2010) for econometric treatment	Wooldridge (textbook treatment); classical least squares
Tip	Nonlinear regression estimator	Linear regression
Sursa seminală	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Denumiri alternative	nonlinear least squares, NLS, NLLS, nonlinear regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	5	5
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).