Comparer des méthodes

	Moindres carrés ordinaires non linéaires (MCO non linéaires)	Estimation par maximum de vraisemblance
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	1974–1987	1922
Auteur d'origine	Gallant (1987); Wooldridge (2010) for econometric treatment	R. A. Fisher
Type	Nonlinear regression estimator	Parametric point estimator
Source fondatrice	Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.
Alias	nonlinear least squares, NLS, NLLS, nonlinear regression	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood
Apparentées	5	4
Résumé	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.	Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.