Võrdle meetodeid
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| Duration Models in Politics× | Elliptiline analüüs× | |
|---|---|---|
| Valdkond≠ | Political Science | Uurimisstatistika |
| Perekond≠ | Regression model | Process / pipeline |
| Tekkeaasta≠ | 1972 | 1958 |
| Looja≠ | David R. Cox (Cox model); popularized in political science by Janet Box-Steffensmeier & Bradford Jones | Edward L. Kaplan and Paul Meier |
| Tüüp≠ | Time-to-event regression model | Method |
| Algallikas≠ | Box-Steffensmeier, J. M., & Jones, B. S. (2004). Event History Modeling: A Guide for Social Scientists. Cambridge University Press. ISBN: 9780521546737 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Rööpnimetused≠ | Event history models, Survival models in political science, Hazard models, Time-to-event models in politics | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Seotud | 3 | 3 |
| Kokkuvõte≠ | Duration models — also called event history or survival models — analyze the time until a political event occurs: how long a cabinet lasts before it falls, how long a war runs before it ends, how long a policy takes to be adopted, or how long a regime survives. Rather than asking only whether an event happens, these models ask when, modeling the hazard rate as a function of covariates while correctly handling censored cases that have not yet experienced the event. The Cox proportional hazards model and parametric alternatives such as the Weibull, popularized in political science by Box-Steffensmeier and Jones, form the core toolkit. | 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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