Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Точне висновування на основі рандомізації Фішера× | Метод ковзного виключення (Jackknife Resampling)× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1935 | 1956 |
| Автор методу≠ | Ronald A. Fisher | Quenouille (1956); reviewed by Miller (1974) |
| Тип≠ | Exact permutation-based inference | Resampling / bias and variance estimation |
| Основоположне джерело≠ | Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd. link ↗ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ |
| Інші назви | fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization) | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions. | 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. |
| ScholarGateНабір даних ↗ |
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