Σύγκριση μεθόδων

	Παλινδρόμηση Αναλογικών Κινδύνων του Cox	Robust Regression
Πεδίο	Ανάλυση Επιβίωσης	Στατιστική
Οικογένεια	Survival analysis	Regression model
Έτος προέλευσης	1972	1964
Δημιουργός	Cox, D. R.	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Τύπος	Semi-parametric hazard regression model	Regression with outlier resistance
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Συναφείς	3	6
Σύνοψη	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.