Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Метод складного ножа (Jackknife Resampling)× | Квантильная регрессия (непараметрические варианты)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1956 | 1978 |
| Автор метода≠ | Quenouille (1956); reviewed by Miller (1974) | Koenker & Bassett |
| Тип≠ | Resampling / bias and variance estimation | Quantile regression (nonparametric variants) |
| Основополагающий источник≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ | Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Другие названия | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar) |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data. |
| ScholarGateНабор данных ↗ |
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