方法对比
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| 贝叶斯随机效应模型× | 混合效应模型× | |
|---|---|---|
| 领域≠ | 计量经济学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1972–1995 | 1982 |
| 提出者≠ | Lindley & Smith (1972); extended by Gelman, Rubin and colleagues | Laird & Ware |
| 类型≠ | Bayesian hierarchical panel model | Mixed effects regression |
| 开创性文献≠ | 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 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| 别名 | Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM | LME, LMM, mixed model, random effects model |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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