Võrdle meetodeid

	Coxi proportsionaalse ohutuse regressioon	Robust Regression
Valdkond	Elukestusanalüüs	Statistika
Perekond	Survival analysis	Regression model
Tekkeaasta	1972	1964
Looja	Cox, D. R.	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Tüüp	Semi-parametric hazard regression model	Regression with outlier resistance
Algallikas	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.
Rööpnimetused	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Seotud	3	6
Kokkuvõte	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.