手法を比較

	頑健なCox回帰	Cox比例ハザード回帰	頑健回帰
分野	統計学	生存時間解析	統計学
系統	Regression model	Survival analysis	Regression model
提唱年	1989	1972	1964
提唱者	Lin & Wei	Cox, D. R.	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
種類	Semi-parametric survival regression with robust variance	Semi-parametric hazard regression model	Regression with outlier resistance
原典	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.	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.
別名	Cox model with robust standard errors, sandwich-variance Cox regression, Lin-Wei robust Cox model, robust partial likelihood regression	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
関連	3	3	6
概要	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.	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.