Comparer des méthodes

	Régression logistique robuste	Régression par Moindres Carrés Ordinaires (MCO)	Régression quantile
Domaine	Statistique	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2001	2019	1978
Auteur d'origine	Cantoni & Ronchetti (2001); Bondell (2008)	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Type	Robust generalized linear model (binary outcome)	Linear regression	Conditional quantile regression
Source fondatrice	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5	5
Résumé	Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.