পদ্ধতির তুলনা করুন

	Robust Cox Regression	দৃঢ় রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1989	1964
প্রবর্তক	Lin & Wei	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
ধরন	Semi-parametric survival regression with robust variance	Regression with outlier resistance
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	3	6
সারসংক্ষেপ	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.