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| Conflict Recurrence Analysis× | Túlélemzési módszerek× | |
|---|---|---|
| Tudományterület≠ | International Relations | Kutatási statisztika |
| Módszercsalád≠ | Survival analysis | Process / pipeline |
| Keletkezés éve≠ | 2004 | 1958 |
| Megalkotó≠ | Civil-war recurrence literature (e.g., Barbara F. Walter) | Edward L. Kaplan and Paul Meier |
| Típus≠ | Survival/repeated-events analysis of renewed conflict | Method |
| Alapmű≠ | Walter, B. F. (2004). Does conflict beget conflict? Explaining recurring civil war. Journal of Peace Research, 41(3), 371–388. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alternatív nevek≠ | Recurring Civil War Analysis, Conflict Relapse Analysis, Repeated-Conflict Survival Analysis, Conflict Recidivism Analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Kapcsolódó | 3 | 3 |
| Összefoglaló≠ | Conflict recurrence analysis studies why and when conflicts that have ended return, treating renewed war as a time-to-event outcome. Most civil wars in recent decades have occurred in countries with a prior war, making recurrence a central puzzle. Using survival and repeated-events models — as in Barbara Walter's (2004) analysis of recurring civil war — researchers model the hazard that a post-conflict country relapses into violence as a function of how the war ended and the underlying conditions, while accounting for the fact that the same country can experience multiple conflict spells. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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