Порівняння методів

	Оцінювання методом ресемплінгу «ковзний ніж» (Jackknife Resampling Estimation)	Крос-валідація	Метод Монте-Карло
Галузь	Статистика	Прийняття рішень	Прийняття рішень
Родина	Hypothesis test	MCDM	MCDM
Рік появи	1956	1974	1949
Автор методу	Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)	Stone, M.	Metropolis, N., Ulam, S.
Тип	Bias and variance estimation	Robustness wrapper — k-fold cross-validation for MCDM stability	Robustness wrapper — Monte Carlo uncertainty propagation
Основоположне джерело	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.	Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Інші назви	delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme	—	—
Пов'язані	3	0	0
Підсумок	Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators.	CROSS-VALIDATION (Cross-Validation — k-fold hold-out validation of MCDM decision consistency) is a ranking multi-criteria decision-making (MCDM) method introduced by Stone, M. in 1974. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.