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| Hierarchische Bootstrap-Simulation× | Hierarchische Bayes'sche Inferenz× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1997-2008 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Urheber≠ | Davison & Hinkley; Cameron, Gelbach & Miller | Lindley & Smith; Gelman et al. |
| Typ≠ | resampling simulation | Bayesian multilevel model |
| Wegweisende Quelle≠ | Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Aliasnamen | cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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