Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Coxin hazard-suhteiden regressiomalli× | Robust Regression× | |
|---|---|---|
| Tieteenala≠ | Elinaika-analyysi | Tilastotiede |
| Menetelmäperhe≠ | Survival analysis | Regression model |
| Syntyvuosi≠ | 1972 | 1964 |
| Kehittäjä≠ | Cox, D. R. | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Tyyppi≠ | Semi-parametric hazard regression model | Regression with outlier resistance |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Liittyvät≠ | 3 | 6 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
|
|