Comparer des méthodes

	Techniques de réduction de variance pour la simulation de Monte-Carlo	Simulation Bootstrap
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1950s–1980s (technique family)	1979
Auteur d'origine	Hammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953)	Bradley Efron
Type	Simulation variance-reduction technique family	Simulation-based nonparametric inference
Source fondatrice	Ross, S.M. (2012). Simulation (5th ed.). Academic Press. ISBN: 978-0124158252	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.
Alias	antithetic variates, control variates, importance sampling, stratified sampling MC	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)
Apparentées	4	5
Résumé	Variance reduction techniques are a family of methods that improve the efficiency of Monte Carlo simulation by achieving the same estimation accuracy with fewer random draws. Developed incrementally from the 1950s onward — with antithetic variates attributed to Hammersley and Morton, control variates formalised by Lavenberg and Welch, and importance sampling rooted in Kahn and Marshall — the family includes antithetic variates (AV), control variates (CV), importance sampling (IS), and stratification, each exploiting a different structural property of the target quantity to lower estimator variance without introducing bias.	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.