Comparar métodos

	Estimación por Jackknife	Validación Cruzada	Simulación de Monte Carlo
Campo	Estadística	Toma de decisiones	Toma de decisiones
Familia	Hypothesis test	MCDM	MCDM
Año de origen	1956	1974	1949
Autor original	Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)	Stone, M.	Metropolis, N., Ulam, S.
Tipo	Bias and variance estimation	Robustness wrapper — k-fold cross-validation for MCDM stability	Robustness wrapper — Monte Carlo uncertainty propagation
Fuente seminal	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
Alias	delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme	—	—
Relacionados	3	0	0
Resumen	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.