Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression Robuste× | Régression de survie× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1964 | 1980s |
| Auteur d'origine≠ | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) | Kalbfleisch & Prentice; Cox & Oakes |
| Type≠ | Regression with outlier resistance | Parametric survival model |
| Source fondatrice≠ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 |
| Alias | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation | accelerated failure time model, AFT model, parametric survival model, time-to-event regression |
| Apparentées≠ | 6 | 3 |
| Résumé≠ | 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. | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. |
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