Comparer des méthodes

	Simulation de bootstrap hiérarchique	Filtre de Kalman
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1997-2008	1960
Auteur d'origine	Davison & Hinkley; Cameron, Gelbach & Miller	Rudolf E. Kalman
Type	resampling simulation	recursive Bayesian filter
Source fondatrice	Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Alias	cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Apparentées	5	5
Résumé	Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of the data by resampling at each level of the hierarchy in sequence, producing a sampling distribution that correctly reflects both between-group and within-group variability.	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.