Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Jackknife-otanta× | Bootstrap-estimaatti× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1956 | 1979 |
| Kehittäjä≠ | Quenouille (1956); reviewed by Miller (1974) | Bradley Efron |
| Tyyppi≠ | Resampling / bias and variance estimation | Resampling-based inference |
| Alkuperäislähde≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Rinnakkaisnimet | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability. | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. |
| ScholarGateAineisto ↗ |
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