방법 비교

	Recidivism Survival Analysis	Kaplan-Meier 추정량
분야	Criminology	통계학
계열	Survival analysis	Survival analysis
기원 연도	1988	1958
창시자	David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application)	Edward L. Kaplan and Paul Meier
유형	Time-to-event regression for reoffending	Nonparametric estimator
원전	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
별칭	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
관련	4	2
요약	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.