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| Recidivism Survival Analysis× | Estimateur de Kaplan-Meier× | |
|---|---|---|
| Domaine≠ | Criminology | Statistique |
| Famille | Survival analysis | Survival analysis |
| Année d'origine≠ | 1988 | 1958 |
| Auteur d'origine≠ | David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application) | Edward L. Kaplan and Paul Meier |
| Type≠ | Time-to-event regression for reoffending | Nonparametric estimator |
| Source fondatrice≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | Time-to-Recidivism Modeling, Recidivism Hazard Modeling, Failure-Time Analysis of Reoffending, Survival Analysis of Reoffending | KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator |
| Apparentées≠ | 4 | 2 |
| Résumé≠ | Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods — Kaplan-Meier curves, Cox proportional-hazards regression, and split-population models — to answer not just whether someone recidivates but how quickly and what raises or lowers that risk over time. | The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research. |
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