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| Μπεϋζιανή Οικολογική Μελέτη× | Πολυεπίπεδη Μοντελοποίηση× | |
|---|---|---|
| Πεδίο≠ | Επιδημιολογία | Ερευνητική Στατιστική |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework) | 1992 |
| Δημιουργός≠ | Andrew Lawson; Julian Besag (spatial Bayesian foundations) | Anthony Bryk and Stephen Raudenbush |
| Τύπος≠ | Observational epidemiological design with Bayesian statistical framework | Method |
| Θεμελιώδης πηγή≠ | Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (2nd ed.). CRC Press. ISBN: 978-1466504813 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Εναλλακτικές ονομασίες | Bayesian ecological analysis, Bayesian disease mapping, Bayesian ecological regression, Bayesian spatial ecological study | HLM, mixed-effects models, random effects models, MLM |
| Συναφείς | 3 | 3 |
| Σύνοψη≠ | A Bayesian ecological study combines the group-level observational design of classical ecological epidemiology with Bayesian hierarchical modelling. Rather than treating disease rates as fixed quantities, it places prior distributions over latent spatial or temporal effects — commonly using the Besag-York-Mollié (BYM) convolution prior — and updates beliefs from aggregate data to produce posterior maps of disease risk, smoothed rate estimates, and credible intervals for ecological associations between exposures and outcomes. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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