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चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Militarized Interstate Dispute Analysis× | उत्तरजीविता विश्लेषण× | |
|---|---|---|
| क्षेत्र≠ | International Relations | अनुसंधान सांख्यिकी |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1996 | 1958 |
| प्रवर्तक≠ | Daniel Jones, Stuart Bremer & J. David Singer (Correlates of War project) | Edward L. Kaplan and Paul Meier |
| प्रकार≠ | Coding and statistical analysis of interstate militarized confrontations | Method |
| मौलिक स्रोत≠ | Jones, D. M., Bremer, S. A., & Singer, J. D. (1996). Militarized interstate disputes, 1816–1992: Rationale, coding rules, and empirical patterns. Conflict Management and Peace Science, 15(2), 163–213. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| उपनाम≠ | MID Analysis, Militarized Dispute Coding, Correlates of War Dispute Analysis, Dyadic Conflict Onset Analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| संबंधित | 3 | 3 |
| सारांश≠ | Militarized interstate dispute (MID) analysis is the coding and quantitative study of confrontations in which one state threatens, displays, or uses military force against another. Built on the Correlates of War project's MID dataset and the coding rules codified by Jones, Bremer, and Singer (1996), it provides the standard observational measure of interstate conflict short of and including war, structured as dyad-years so that the onset, escalation, and outcomes of disputes can be modeled statistically across two centuries of the international system. | 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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