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| Inferens Bootstrap× | Resampling Jackknife× | Ujian Permutasi (Pemerolehan Rawak)× | |
|---|---|---|---|
| Bidang | Statistik | Statistik | Statistik |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1979 | 1956 | 2005 |
| Pengasas≠ | Bradley Efron | Quenouille (1956); reviewed by Miller (1974) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Jenis≠ | Resampling-based inference | Resampling / bias and variance estimation | Nonparametric resampling test |
| Sumber perintis≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Alias | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | 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. | 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. | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. |
| ScholarGateSet data ↗ |
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