Comparer des méthodes

	Régression proportionnelle des risques de Cox	Régression Robuste
Domaine	Analyse de survie	Statistique
Famille	Survival analysis	Regression model
Année d'origine	1972	1964
Auteur d'origine	Cox, D. R.	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Semi-parametric hazard regression model	Regression with outlier resistance
Source fondatrice	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	3	6
Résumé	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.