方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 稳健 Cox 回归× | 稳健回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1989 | 1964 |
| 提出者≠ | Lin & Wei | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| 类型≠ | Semi-parametric survival regression with robust variance | Regression with outlier resistance |
| 开创性文献≠ | 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. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| 别名 | 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 |
| 相关≠ | 3 | 6 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
|
|