Vertaile menetelmiä

	Robustin Coxin regressio	Robust Regression
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1989	1964
Kehittäjä	Lin & Wei	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Tyyppi	Semi-parametric survival regression with robust variance	Regression with outlier resistance
Alkuperäislähde	Lin, D. Y., & Wei, L. J. (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association, 84(408), 1074–1078.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Rinnakkaisnimet	Cox model with robust standard errors, sandwich-variance Cox regression, Lin-Wei robust Cox model, robust partial likelihood regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Liittyvät	3	6
Tiivistelmä	Robust Cox regression fits the standard Cox proportional hazards model but replaces the model-based variance estimate with a sandwich (Huber-White) estimator. This yields valid standard errors and confidence intervals even when observations are clustered, the independence assumption is mildly violated, or the working model is slightly misspecified, without discarding the familiar hazard-ratio interpretation.	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.