Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Bayesovský model náhodných efektov× | Hierarchický lineárny model (HLM)× | |
|---|---|---|
| Odbor≠ | Ekonometria | Štatistika |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1972–1995 | 1992 |
| Tvorca≠ | Lindley & Smith (1972); extended by Gelman, Rubin and colleagues | Bryk & Raudenbush |
| Typ≠ | Bayesian hierarchical panel model | Multilevel linear regression |
| Pôvodný zdroj≠ | 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 | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 |
| Ďalšie názvy | Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM | HLM, multilevel linear model, nested data model, random coefficient model |
| Príbuzné≠ | 5 | 4 |
| Zhrnutie≠ | The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable. | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. |
| ScholarGateDátová sada ↗ |
|
|