Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Koksas proporcionālās bīstamības regresija× | Robustā regresija× | |
|---|---|---|
| Nozare≠ | Dzīvildze | Statistika |
| Saime≠ | Survival analysis | Regression model |
| Izcelsmes gads≠ | 1972 | 1964 |
| Autors≠ | Cox, D. R. | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Tips≠ | Semi-parametric hazard regression model | Regression with outlier resistance |
| Pirmavots≠ | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Citi nosaukumi | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Saistītās≠ | 3 | 6 |
| Kopsavilkums≠ | 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. |
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