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| Regresión bayesiana× | Estimador de Supervivencia de Kaplan-Meier× | |
|---|---|---|
| Campo≠ | Bayesiano | Supervivencia |
| Familia≠ | Bayesian methods | Survival analysis |
| Año de origen≠ | — | 1958 |
| Autor original≠ | — | Kaplan, E. L. & Meier, P. |
| Tipo≠ | Bayesian linear model | Non-parametric survival estimator |
| Fuente seminal≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | bayesian linear regression, probabilistic regression, bayesian regresyon | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Relacionados | 2 | 2 |
| Resumen≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups. |
| ScholarGateConjunto de datos ↗ |
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