Сравнение методов

	Скорректированная ящичковая диаграмма для асимметричных распределений	Бутстреп-вывод	Метод складного ножа (Jackknife Resampling)
Область	Статистика	Статистика	Статистика
Семейство	Regression model	Regression model	Regression model
Год появления	2008	1979	1956
Автор метода	Hubert & Vandervieren	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)
Тип	Robust outlier detection / descriptive visualization	Resampling-based inference	Resampling / bias and variance estimation
Основополагающий источник	Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.
Другие названия	adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme
Связанные	5	5	5
Сводка	The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data.	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.