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| Jackknife Resampling Estimation× | MONTE-CARLO-SIMULATION× | |
|---|---|---|
| Tudományterület≠ | Statisztika | Döntéshozatal |
| Módszercsalád≠ | Hypothesis test | MCDM |
| Keletkezés éve≠ | 1956 | 1949 |
| Megalkotó≠ | Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming) | Metropolis, N., Ulam, S. |
| Típus≠ | Bias and variance estimation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Alapmű≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alternatív nevek≠ | delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme | — |
| Kapcsolódó≠ | 3 | 0 |
| Összefoglaló≠ | Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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