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| Multilevel-Bootstrap-Simulation× | Hierarchische Bayes'sche Inferenz× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1979 (bootstrap); multilevel variants c.1990s | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Urheber≠ | Efron (1979); multilevel extensions developed through 1980s–2000s | Lindley & Smith; Gelman et al. |
| Typ≠ | resampling / simulation | Bayesian multilevel model |
| Wegweisende Quelle≠ | Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. DOI ↗ | 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 | hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Multilevel bootstrap simulation is a resampling technique designed for clustered or hierarchically structured data. It preserves the nested data structure by resampling at each level independently — first drawing clusters (e.g., schools, hospitals), then drawing observations within each sampled cluster — so that bootstrap replicate datasets reflect the same multilevel organisation as the original data. | 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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