Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regresija izdzīvošanai× | Veibula parametriskā izdzīvošanas regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Dzīvildze |
| Saime≠ | Regression model | Survival analysis |
| Izcelsmes gads≠ | 1980s | 1951 |
| Autors≠ | Kalbfleisch & Prentice; Cox & Oakes | Waloddi Weibull |
| Tips≠ | Parametric survival model | Fully parametric survival regression model |
| Pirmavots≠ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| Citi nosaukumi | accelerated failure time model, AFT model, parametric survival model, time-to-event regression | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival. |
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